Modeling Mediocrity

2012 Economic Indicator Forecast

2011-12-30T06:32:00.000-08:00

In 2011, over several weeks, I posted a model for each of the most vital economic indicators: GDP, Unemployment, Inflation, federal budget, federal debt, federal spending, trade deficit, consumer spending, tax receipts, state deficits, state social benefit spending, personal income, and federal social benefit spending. From these models I am predicting the 2012 end of year results for these variables to be as follows (2010 results are in parenthesis):

Federal Social Benefits – 2.5 trillion dollars (2.3)

Personal Income – 12.9 trillion dollars (12.5)

State Social Benefits – 615 billion dollars (540)

State Deficits – 87 billion dollars (50)

Consumer Spending – 11 trillion dollars (10.5)

Trade Deficit – 560 billion dollars (520)

Budget – 3.7 trillion dollars (3.7)

Government Spending – 5.1 trillion dollars (5.3)

Tax Receipts – 3.7 trillion dollars (3.9)

Federal Debt – 16.4 trillion dollars (13.3)

Inflation – 2% (1)

Unemployment – 7.5% (9.6)

GDP – 15.5 trillion dollars (14.2)

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

A Summary of CO2 Models

2011-11-29T17:25:00.000-08:00

Over the past year I have been blogging continuously over the issue of “manmade” climate change. First, I do not believe that manmade climate change is happening, but I do believe that carbon dioxide (CO2) emissions can be dangerous in large amounts. Thus, I have been researching to find a smart solution to combat CO2 without overreacting and destroying the economy with cap and trade policies. Here is summary of my past posts:

I ran a linear regression model to understand the relationship between global temperatures, carbon dioxide in our atmosphere, the cost of electricity, and energy consumption when increasing our reliance on renewable energies (wind and solar) by 25% and reducing our reliance on fossil fuels by 25%. In parenthesis are the results of increasing our reliance on nuclear energy by 25% instead of wind and solar.

Carbon emissions in our atmosphere increased from 391 to 428 (391 to 414) parts per million (ppm)
The global temperature decreased from 0.7 degrees to -1.84 (increase from 0.7 to 1) degrees from our 1980 global temperature levels
Energy consumption in the United States decreased 3 times (increase 25%)
The cost of energy increased by 38% (decrease by 22%)
The increased nuclear energy usage analysis proves there is little that can be done to combat global warming meanwhile; the increased wind and solar energy usage analysis proves the relationship between global temperatures and CO2 emissions are not necessarily directly proportional as global warming alarmists insist.

These results show there is no directly proportional relationship between global temperatures and CO2 emissions. The fact that CO2 is always increasing in our environment follows Thermodynamics Second Law that states the entropy in the universe is always increasing. Entropy measures the disorder or imperfections of a system. In other words, all products and life forms have imperfections. For instance, a human being and car have imperfections and emit carbon byproducts. Even if the world miraculously used green energy 90% of the time, CO2 emissions in our atmosphere will continue to increase. Thus, the billions of dollars that global governments are spending to make their power grids smart and to convert to renewable energies is a total waste of time and money. Thankfully, there are a few climate scientists that have some commonsense: Klaus Lackner from Columbia University and David Keith from Calgary University. They are developing promising carbon scrubbing technology that will not affect our lifestyles one bit while at the same time lowering CO2 levels in our atmosphere. Here are links to their studies:

http://www.physorg.com/news141915261.html

http://www.earthinstitute.columbia.edu/articles/view/2523

There is absolutely nothing wrong with being a conservative that is conscience of their carbon footprint for a few simple reasons. Reducing our carbon footprint lessens the amount of pollutants that enter the air we breathe and secondly, it can save us hundreds of dollars annually in energy bills. The Environmental Protection Agency (EPA) has an online calculator so each individual can compute their CO2 emissions. In other words, this calculator reveals the carbon footprint of each individual and family. This site is located at: http://epa.gov/climatechange/emissions/ind_calculator.html.

For more detailed information about global warming, climate change, and carbon emissions please refer to my earlier blogs: A Commonsense Solution to CO2 (Parts I through IV) and The Relationship between Energy Costs, Global Temperatures, CO2 Emissions, and Energy Consumption (Parts I through VI)

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumption (Part VIII)

2011-11-29T06:28:00.000-08:00

Below are the results for Energy Cost and Energy Consumption with the U.S. consuming more 25% more nuclear energy and 25% less fossil fuels (Oil, Coal, and Natural Gas).

Energy Cost
R Square	0.99
SE	1.60E-01

Coefficients	Coefficient Value	Value	Energy Cost		Ave	Energy Cost
Intercept	-54.18	1.00E+00	-5.42E+01		1.00E+00	-54.18
Coal KWH1	7.484	1.48E+00	1.11E+01		9.00E-01	6.7356
NG KWH1	2.443	4.71E+00	1.15E+01		3.10E+00	7.5733
Oil KWH1	3.494	1.85E+00	6.46E+00		1.15E+00	4.0181
NP KWH1	12.32	2.30E-01	2.83E+00		7.00E-01	8.624
Hydro KWH1	2.26	3.67E-01	8.29E-01		3.67E-01	0.82942
Geo KWH1	-6.728	9.20E-02	-6.19E-01		9.20E-02	-0.618976
Solar KWh1	13.88	4.17E-02	5.79E-01		4.17E-02	0.578796
Wind KWH1	4.848	1.43E-01	6.93E-01		1.43E-01	0.693264
CO2 GDP	0.003283	5.10E+02	1.67E+00		4.80E+02	1.57584
CO2 GDP1	-0.00312	4.16E+02	-1.30E+00		3.90E+02	-1.2168
USP	-32.38	1.80E-01	-5.83E+00		1.70E-01	-5.5046
Temp	-0.7734	7.00E-01	-5.41E-01		1.00E+00	-0.7734
Biomass KWH1	2.213	3.21E-01	7.10E-01		3.21E-01	0.710373
Energy ConsP	4.87E-08	9.46E+07	4.61E+00		1.10E+08	5.35777
CO2	0.08585	3.90E+02	3.35E+01		4.15E+02	35.62775
Temp5	-2.971	6.50E-01	-1.93E+00		7.50E-01	-2.22825
Result			1.01E+01			7.802187


Energy Consumption
R Square	1
SE	6.70E+05

Coefficients	Coefficient Value	Value	Energy Consumption	Ave	Energy Consumption
Intercept	378255947	1.00E+00	3.78E+08		1.00E+00	378255947
Coal KWH1	-66478559	1.48E+00	-9.84E+07		9.00E-01	-59830703.1
NG KWH1	-21325244	4.71E+00	-1.00E+08		3.10E+00	-66108256.4
Oil KWH1	-42947504	1.85E+00	-7.95E+07		1.15E+00	-49389629.6
NP KWH1	-176333962	2.30E-01	-4.06E+07		7.00E-01	-123433773.4
Hydro KWH1	-32454908	3.67E-01	-1.19E+07		3.67E-01	-11910951.24
Geo KWH1	-10174301	9.20E-02	-9.36E+05		9.20E-02	-936035.692
Solar KWh1	-79532965	4.17E-02	-3.32E+06		4.17E-02	-3316524.641
Wind KWH1	-109678477	1.43E-01	-1.57E+07		1.43E-01	-15684022.21
CO2 GDP	-9505	5.10E+02	-4.85E+06		4.80E+02	-4562400
CO2 GDP1	25085	4.16E+02	1.04E+07		3.90E+02	9783150
USP	35774485	1.80E-01	6.44E+06		1.70E-01	6081662.45
Temp	1003424	7.00E-01	7.02E+05		1.00E+00	1003424
Biomass KWH1	-8995579	3.21E-01	-2.89E+06		3.21E-01	-2887580.859
Energy Cost	8.46E+05	1.00E+01	8.46E+06		8.00E+00	6771080
CO2	100280	3.90E+02	3.91E+07		4.15E+02	41616200
Temp5	14160839	6.50E-01	9.20E+06		7.50E-01	10620629.25
Result			9.42E+07			116072215.6

These results are also what one may suspect. Energy costs will go down over 20%, but at the same time energy consumption will go up over 20%. When electricity costs go down consumption goes up since people have little incentive to save energy. This may be the best argument for using wind and solar energy since their higher costs force people to consume less energy.

What does this analysis on nuclear energy show or prove? It shows that any attempt of the United States to move to cleaner energy sources does very little to combat global warming and CO2 emissions. The reason for this is simple, the earth is warming normally and there is nothing anyone can do about that trend.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumption (Part VII)

2011-11-26T06:49:00.000-08:00

To be fair to solar and wind power, I performed a similar analysis on the same model except this time we kept solar and wind usage constant while increasing the use of nuclear power by 25%. Oil and coal usage were each reduced 10% and natural gas usage was reduced 5% (same as in the previous model analysis of increasing wind and solar energy usage). Below are the results solving for CO2 emissions in our atmosphere and the annual and five year average of global temperatures.

CO2
R Square	1
SE	6.20E-01

Coefficients	Coefficient Value	Value	CO2 Levels	Ave	CO2 Levels
Intercept	391.7	1.00E+00	3.92E+02	1.00E+00	391.7
Coal KWH1	-7.136	1.48E+00	-1.06E+01	9.00E-01	-6.4224
NG KWH1	0.4164	4.71E+00	1.96E+00	3.10E+00	1.29084
Oil KWH1	-4.139	1.85E+00	-7.66E+00	1.15E+00	-4.75985
NP KWH1	29.63	2.30E-01	6.81E+00	7.00E-01	20.741
Hydro KWH1	5.829	3.67E-01	2.14E+00	3.67E-01	2.139243
Geo KWH1	25.52	9.20E-02	2.35E+00	9.20E-02	2.34784
Solar KWh1	-41.16	4.17E-02	-1.72E+00	4.17E-02	-1.716372
Wind KWH1	41.13	1.43E-01	5.88E+00	1.43E-01	5.88159
CO2 GDP	-0.05882	5.10E+02	-3.00E+01	4.80E+02	-28.2336
CO2 GDP1	-0.001466	4.16E+02	-6.10E-01	3.90E+02	-0.57174
USP	-7.274	1.80E-01	-1.31E+00	1.70E-01	-1.23658
Energy Cost	1.29	1.00E+01	1.29E+01	8.00E+00	10.32
Biomass KWH1	11.45	3.21E-01	3.68E+00	3.21E-01	3.67545
Energy ConsP	8.67E-08	9.46E+07	8.20E+00	1.10E+08	9.53832
Temp	3.437	7.00E-01	2.41E+00	1.00E+00	3.437
Temp1	6.772	6.50E-01	4.40E+00	7.50E-01	5.079
Result			3.91E+02		413.209741

Temp5
R Square	0.99
SE	3.00E-02

Coefficients	Coefficient Value	Value	Temperature	Ave	Temperature
Intercept	-23.22	1.00E+00	-2.32E+01	1.00E+00	-23.22
Coal KWH1	3.359	1.48E+00	4.97E+00	9.00E-01	3.0231
NG KWH1	1.007	4.71E+00	4.74E+00	3.10E+00	3.1217
Oil KWH1	1.93	1.85E+00	3.57E+00	1.15E+00	2.2195
NP KWH1	8.649	2.30E-01	1.99E+00	7.00E-01	6.0543
Hydro KWH1	1.534	3.67E-01	5.63E-01	3.67E-01	0.562978
Geo KWH1	0.1988	9.20E-02	1.83E-02	9.20E-02	0.0182896
Solar KWh1	3.739	4.17E-02	1.56E-01	4.17E-02	0.1559163
Wind KWH1	2.843	1.43E-01	4.07E-01	1.43E-01	0.406549
CO2 GDP	-0.000432	5.10E+02	-2.20E-01	4.80E+02	-0.20736
CO2 GDP1	0.0002631	4.16E+02	1.09E-01	3.90E+02	0.102609
USP	-2.602	1.80E-01	-4.68E-01	1.70E-01	-0.44234
Energy Cost	-0.102	1.00E+01	-1.02E+00	8.00E+00	-0.816
Biomass KWH1	1.463	3.21E-01	4.70E-01	3.21E-01	0.469623
Energy ConsP	2.80E-08	9.46E+07	2.65E+00	1.10E+08	3.07571
CO2	0.01546	3.90E+02	6.03E+00	4.15E+02	6.4159
Temp	-0.09777	7.00E-01	-6.84E-02	1.00E+00	-0.09777
Result			6.74E-01		0.8427049



Temp
R Square	0.92
SE	9.50E-02

Coefficients	Coefficient Value	Value	Temperature	Ave	Temperature
Intercept	-71.98	1.00E+00	-7.20E+01	1.00E+00	-71.98
Coal KWH1	9.077	1.48E+00	1.34E+01	9.00E-01	8.1693
NG KWH1	2.671	4.71E+00	1.26E+01	3.10E+00	8.2801
Oil KWH1	4.071	1.85E+00	7.53E+00	1.15E+00	4.68165
NP KWH1	21.33	2.30E-01	4.91E+00	7.00E-01	14.931
Hydro KWH1	3.985	3.67E-01	1.46E+00	3.67E-01	1.462495
Geo KWH1	-4.161	9.20E-02	-3.83E-01	9.20E-02	-0.382812
Solar KWh1	4.266	4.17E-02	1.78E-01	4.17E-02	0.1778922
Wind KWH1	4.449	1.43E-01	6.36E-01	1.43E-01	0.636207
CO2 GDP	0.005277	5.10E+02	2.69E+00	4.80E+02	2.53296
CO2 GDP1	-0.0009683	4.16E+02	-4.03E-01	3.90E+02	-0.377637
USP	-5.521	1.80E-01	-9.94E-01	1.70E-01	-0.93857
Energy Cost	-0.2749	1.00E+01	-2.75E+00	8.00E+00	-2.1992
Biomass KWH1	2.388	3.21E-01	7.67E-01	3.21E-01	0.766548
Energy ConsP	2.05E-08	9.46E+07	1.94E+00	1.10E+08	2.25786
CO2	0.0813	3.90E+02	3.17E+01	4.15E+02	33.7395
Temp5	-1.013	6.50E-01	-6.58E-01	7.50E-01	-0.75975
Result			6.68E-01		0.9975432

These results indicate that carbon emissions in our atmosphere will increase to over 410 parts per million and global temperatures will increase dramatically from around 0.7 degrees to .85 and 1 degree respectively for the 5 year average and the annual reading. In other words, moving to an energy plan using more nuclear power will do very little to combat the current trends of increasing CO2 emissions and global temperatures. This would be expected since this analysis is just a very small percentage of the overall global energy consumption. The overall analysis of consuming more nuclear energy will be completed tomorrow.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumption (Part VI)

2011-11-23T05:55:00.000-08:00

Below is the result of running a linear regression analysis on the model posted in Part I of this series of blogs solving for global carbon dioxide levels in our atmosphere (CO2).

R²

1.00

Adjusted R²

1.00

0.619

Term

Coefficient

95% CI

t statistic

Intercept

391.7

-155.6

to 939.1

241.95

1.62

0.1399

Temp

3.437

-0.725

to 7.598

1.8397

1.87

0.0946

Temp5

6.772

-8.159

to 21.703

6.6002

1.03

0.3317

Energy Cost

1.29

-1.47

to 4.05

1.218

1.06

0.3173

Energy Cons

8.6712E-08

-6.1142E-07

to 7.8484E-07

3.0861E-007

0.28

0.7851

USP

-7.274

-132.694

to 118.145

55.4423

-0.13

0.8985

CO2 GDP

-0.05882

-0.12638

to 0.00873

0.029862

-1.97

0.0804

CO2 GDP1

-0.001466

-0.071025

to 0.068093

0.0307489

-0.05

0.9630

Wind KWH1

41.13

-59.46

to 141.73

44.469

0.92

0.3791

Solar KWH1

-41.16

-222.38

to 140.06

80.109

-0.51

0.6198

Geo KWH1

25.52

-61.49

to 112.52

38.461

0.66

0.5236

Hydro KWH1

5.829

-50.193

to 61.852

24.7651

0.24

0.8192

BIO KWH1

11.45

-76.58

to 99.47

38.913

0.29

0.7753

NP KWH1

29.63

-269.11

to 328.38

132.061

0.22

0.8275

Oil KWH1

-4.139

-55.000

to 46.721

22.4831

-0.18

0.8580

NG KWH1

0.4164

-34.1482

to 34.9810

15.27949

0.03

0.9789

Coal KWH1

-7.136

-110.263

to 95.991

45.5879

-0.16

0.8791

Source of variation	Sum squares	DF	Mean square	F statistic	p
Model	4525.943	16	282.871	737.33	<0.0001
Residual	3.453	9	0.384
Total	4529.395	25

Coefficients	Coefficient Value	Value	CO2 Levels	Ave	CO2 Levels
Intercept	391.7	1.00E+00	3.92E+02	1.00E+00	391.7
Coal KWH1	-7.136	1.48E+00	-1.06E+01	9.00E-01	-6.4224
NG KWH1	0.4164	4.71E+00	1.96E+00	3.10E+00	1.29084
Oil KWH1	-4.139	1.85E+00	-7.66E+00	1.15E+00	-4.75985
NP KWH1	29.63	2.30E-01	6.81E+00	2.30E-01	6.8149
Hydro KWH1	5.829	3.67E-01	2.14E+00	3.67E-01	2.139243
Geo KWH1	25.52	9.20E-02	2.35E+00	9.20E-02	2.34784
Solar KWh1	-41.16	4.17E-02	-1.72E+00	5.00E-01	-20.58
Wind KWH1	41.13	1.43E-01	5.88E+00	1.40E+00	57.582
CO2 GDP	-0.05882	5.10E+02	-3.00E+01	4.80E+02	-28.2336
CO2 GDP1	-0.001466	4.16E+02	-6.10E-01	3.90E+02	-0.57174
USP	-7.274	1.80E-01	-1.31E+00	1.50E-01	-1.0911
Energy Cost	1.29	1.00E+01	1.29E+01	1.00E+01	12.9
Biomass KWH1	11.45	3.21E-01	3.68E+00	3.21E-01	3.67545
Energy ConsP	8.67E-08	9.46E+07	8.20E+00	5.50E+07	4.76916
Temp	3.437	7.00E-01	2.41E+00	7.00E-01	2.4059
Temp1	6.772	6.50E-01	4.40E+00	6.50E-01	4.4018
Result			3.91E+02		428.368443

The model has excellent correlation as shown by the R² variable equal to 1. The results indicate that the 2009 value for CO2 levels in our atmosphere can increase from 390 parts per million to 428 parts per million by decreasing the United States reliance on coal, natural gas, and oil by 25% and increasing the United States reliance on Solar and Wind energy by 25% (highlighted in red). Nuclear power, hydro power, biomass, and geothermal variables were held constant. Other 2009 variables were adjusted to keep up with the trends of using renewable energies such as increased energy costs, higher global temperatures (Temp and Temp5 – even with lower CO2 emissions the global temperature trend is upward), lower energy consumption (including the USP variable), and lower CO2 to GDP numbers.

This result makes a lot of sense since an increased reliance of renewable energy sources in the United States will not affect global emissions very much since the United States only accounts for 16% of the total greenhouse gases emitted into the atmosphere. Besides, even a massive increase in the global usage of renewable energy sources will never completely eliminate carbon dioxide emissions into the atmosphere.

The best conclusion that can be drawn from this model is that while CO2 emissions increased by nearly 10% in our atmosphere; the model indicates that global temperatures would decline. This violates the hypothesis created by the global warming community that CO2 emissions are making the earth warmer. Keep in mind that models are only as good as the variables, correlation, and trends that currently exist. If, for instance, a trend changes, it can render any model useless. But the bottom line is it easy to build a model that contradicts the findings of climate change scientists.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumption (Part V)

2011-11-22T05:52:00.000-08:00

Below is the result of running a linear regression analysis on the model posted in Part I of this series of blogs solving for energy costs.

R²	0.99
Adjusted R²	0.98
SE	0.160

Term	Coefficient	95% CI	SE	t statistic	DF	p
Intercept	-54.18	-209.34	to 100.98	68.589	-0.79	9	0.4499
Temp	-0.7734	-1.8957	to 0.3489	0.49610	-1.56	9	0.1534
Temp5	-2.971	-6.370	to 0.427	1.5024	-1.98	9	0.0794
CO2	0.08585	-0.09759	to 0.26929	0.081091	1.06	9	0.3173
Energy Cons	4.8707E-08	-1.2841E-07	to 2.2583E-07	7.8298E-008	0.62	9	0.5493
USP	-32.38	-53.66	to -11.10	9.405	-3.44	9	0.0074
CO2 GDP	0.003283	-0.017417	to 0.023984	0.0091507	0.36	9	0.7280
CO2 GDP1	-0.00312	-0.02091	to 0.01467	0.007865	-0.40	9	0.7008
Coal KWH1	7.484	-18.552	to 33.520	11.5094	0.65	9	0.5318
NG KWH1	2.443	-6.282	to 11.168	3.8569	0.63	9	0.5422
Oil KWH1	3.494	-9.384	to 16.373	5.6931	0.61	9	0.5545
NP KWH1	12.32	-64.40	to 89.05	33.916	0.36	9	0.7247
BIO KWH1	2.213	-20.543	to 24.970	10.0598	0.22	9	0.8308
Hydro KWH1	2.26	-12.14	to 16.66	6.364	0.36	9	0.7307
Geo KWH1	-6.728	-29.148	to 15.693	9.9112	-0.68	9	0.5143
Solar KWH1	13.88	-32.38	to 60.15	20.450	0.68	9	0.5142
Wind KWH1	4.848	-22.062	to 31.758	11.8956	0.41	9	0.6931

Source of variation	Sum squares	DF	Mean square	F statistic	p
Model	27.078	16	1.692	66.28	<0.0001
Residual	0.230	9	0.026
Total	27.308	25

Coefficients	Coefficient Value	Value	Energy Cost	Ave	Energy Cost
Intercept	-54.18	1.00E+00	-5.42E+01	1.00E+00	-54.18
Coal KWH1	7.484	1.48E+00	1.11E+01	9.00E-01	6.7356
NG KWH1	2.443	4.71E+00	1.15E+01	3.10E+00	7.5733
Oil KWH1	3.494	1.85E+00	6.46E+00	1.15E+00	4.0181
NP KWH1	12.32	2.30E-01	2.83E+00	2.30E-01	2.8336
Hydro KWH1	2.26	3.67E-01	8.29E-01	3.67E-01	0.82942
Geo KWH1	-6.728	9.20E-02	-6.19E-01	9.20E-02	-0.618976
Solar KWh1	13.88	4.17E-02	5.79E-01	5.00E-01	6.94
Wind KWH1	4.848	1.43E-01	6.93E-01	1.40E+00	6.7872
CO2 GDP	0.003283	5.10E+02	1.67E+00	4.80E+02	1.57584
CO2 GDP1	-0.00312	4.16E+02	-1.30E+00	3.90E+02	-1.2168
USP	-32.38	1.80E-01	-5.83E+00	1.60E-01	-5.1808
Temp	-0.7734	7.00E-01	-5.41E-01	7.00E-01	-0.54138
Biomass KWH1	2.213	3.21E-01	7.10E-01	3.21E-01	0.710373
Energy ConsP	4.87E-08	9.46E+07	4.61E+00	5.50E+07	2.678885
CO2	0.08585	3.90E+02	3.35E+01	4.30E+02	36.9155
Temp5	-2.971	6.50E-01	-1.93E+00	7.00E-01	-2.0797
Result			1.01E+01		13.780162

The model has excellent correlation as shown by the R² variable equal to 0.99. The results indicate that the 2009 value for energy costs can increase from 10 cents per kilowatt – hour to 13.8 cents per kilowatt - hour by decreasing the United States reliance on coal, natural gas, and oil by 25% and increasing the United States reliance on Solar and Wind energy by 25% (highlighted in red). Nuclear power, hydro power, biomass, and geothermal variables were held constant. Other 2009 variables were adjusted to keep up with the trends of using renewable energies such as increased energy costs, higher global temperatures (Temp and Temp5 – even with lower CO2 emissions the global temperature trend is upward), lower energy consumption (including the USP variable), and lower CO2 to GDP numbers.

This result makes sense (38% increase in energy costs) since renewable energy sources are much more expensive. And what’s worse this analysis does not include potential energy cost increases if cap and trade legislation passes.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumption (Part IV)

2011-11-19T06:45:00.000-08:00

Below is the result of running a linear regression analysis on the model posted in Part I of this series of blogs solving for global temperatures (Temp5). This simulation is similar to the one done in Part III except this time the temperature variable being evaluated contains the running five year average of global temperatures.

R²	0.99
Adjusted R²	0.97
SE	0.030

Term	Coefficient	95% CI	SE	t statistic	DF	p
Intercept	-23.22	-47.24	to 0.79	10.615	-2.19	9	0.0564
Temp	-0.09777	-0.32014	to 0.12460	0.098301	-0.99	9	0.3459
CO2	0.01546	-0.01863	to 0.04956	0.015072	1.03	9	0.3317
Energy Cost	-0.102	-0.219	to 0.015	0.0516	-1.98	9	0.0794
USP	-2.602	-8.271	to 3.067	2.5060	-1.04	9	0.3261
Energy Cons	2.7961E-08	1.9196E-09	to 5.4003E-08	1.1512E-008	2.43	9	0.0381
CO2 GDP	-0.000432	-0.004280	to 0.003416	0.0017010	-0.25	9	0.8052
CO2 GDP1	0.0002631	-0.0030553	to 0.0035816	0.00146694	0.18	9	0.8616
Coal KWH1	3.359	-0.876	to 7.594	1.8722	1.79	9	0.1064
NG KWH1	1.007	-0.460	to 2.474	0.6485	1.55	9	0.1549
Oil KWH1	1.93	-0.02	to 3.88	0.863	2.24	9	0.0521
NP KWH1	8.649	-4.094	to 21.393	5.6334	1.54	9	0.1591
BIO KWH1	1.463	-2.617	to 5.543	1.8036	0.81	9	0.4382
Hydro KWH1	1.534	-0.890	to 3.957	1.0713	1.43	9	0.1860
Geo KWH1	0.1998	-4.0556	to 4.4552	1.88113	0.11	9	0.9177
Solar KWH1	3.759	-4.558	to 12.075	3.6762	1.02	9	0.3333
Wind KWH1	2.843	-1.707	to 7.394	2.0116	1.41	9	0.1912

Source of variation	Sum squares	DF	Mean square	F statistic	p
Model	0.737	16	0.046	52.60	<0.0001
Residual	0.008	9	0.001
Total	0.745	25

Coefficients	Coefficient Value	Value	Temperature	Ave	Temperature
Intercept	-23.22	1.00E+00	-2.32E+01	1.00E+00	-23.22
Coal KWH1	3.359	1.48E+00	4.97E+00	9.00E-01	3.0231
NG KWH1	1.007	4.71E+00	4.74E+00	3.10E+00	3.1217
Oil KWH1	1.93	1.85E+00	3.57E+00	1.15E+00	2.2195
NP KWH1	8.649	2.30E-01	1.99E+00	2.30E-01	1.98927
Hydro KWH1	1.534	3.67E-01	5.63E-01	3.67E-01	0.562978
Geo KWH1	0.1988	9.20E-02	1.83E-02	9.20E-02	0.0182896
Solar KWh1	3.739	4.17E-02	1.56E-01	5.00E-01	1.8695
Wind KWH1	2.843	1.43E-01	4.07E-01	1.40E+00	3.9802
CO2 GDP	-0.000432	5.10E+02	-2.20E-01	4.80E+02	-0.20736
CO2 GDP1	0.0002631	4.16E+02	1.09E-01	3.90E+02	0.102609
USP	-2.602	1.80E-01	-4.68E-01	1.60E-01	-0.41632
Energy Cost	-0.102	1.00E+01	-1.02E+00	1.00E+01	-1.02
Biomass KWH1	1.463	3.21E-01	4.70E-01	3.21E-01	0.469623
Energy ConsP	2.80E-08	9.46E+07	2.65E+00	5.50E+07	1.537855
CO2	0.01546	3.90E+02	6.03E+00	4.30E+02	6.6478
Temp	-0.09777	7.00E-01	-6.84E-02	7.00E-01	-0.068439
Result			6.74E-01		0.6103056

The model has excellent correlation as shown by the R² variable equal to 0.99. The results indicate that the 2009 value for the 5 year average of global temperature can decrease from 0.7 degrees to .6 degrees by decreasing the United States reliance on coal, natural gas, and oil by 25% and increasing the United States reliance on Solar and Wind energy by 25% (highlighted in red). Nuclear power, hydro power, biomass, and geothermal variables were held constant. Other 2009 variables were adjusted to keep up with the trends of using renewable energies such as increased energy costs, higher global temperatures (Temp – even with lower CO2 emissions the global temperature trend is upward), lower energy consumption (including the USP variable), and lower CO2 to GDP numbers.

Global warming alarmist may look at this result as proof that moving to renewable energy sources will eliminate CO2 and therefore, reduce global temperatures. This, however, is not true and this result can be misleading as will be revealed in future blog posts evaluating this model further.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumption (Part III)

2011-11-16T06:07:00.000-08:00

Below is the result of running a linear regression analysis on the model posted in Part I of this series of blogs solving for global temperatures (Temp).

R²	0.92
Adjusted R²	0.78
SE	0.095

Term	Coefficient	95% CI	SE	t statistic	DF	p
Intercept	-71.98	-150.75	to 6.79	34.821	-2.07	9	0.0687
Temp5	-1.013	-3.317	to 1.291	1.0184	-0.99	9	0.3459
CO2	0.0813	-0.0172	to 0.1798	0.04352	1.87	9	0.0946
Energy Cost	-0.2749	-0.6738	to 0.1240	0.17635	-1.56	9	0.1534
USP	-5.521	-24.376	to 13.334	8.3349	-0.66	9	0.5243
Energy Cons	2.0526E-08	-8.6206E-08	to 1.2726E-07	4.7182E-008	0.44	9	0.6738
CO2 GDP	0.005277	-0.006499	to 0.017052	0.0052055	1.01	9	0.3372
CO2 GDP1	-0.0009683	-0.0116435	to 0.0097068	0.00471901	-0.21	9	0.8420
Coal KWH1	9.077	-5.256	to 23.410	6.3360	1.43	9	0.1858
NG KWH1	2.671	-2.250	to 7.591	2.1751	1.23	9	0.2507
Oil KWH1	4.071	-3.140	to 11.282	3.1878	1.28	9	0.2336
NP KWH1	21.33	-21.85	to 64.51	19.088	1.12	9	0.2928
BIO KWH1	2.338	-11.152	to 15.827	5.9631	0.39	9	0.7042
Hydro KWH1	3.985	-4.120	to 12.089	3.5825	1.11	9	0.2949
Geo KWH1	-4.161	-17.502	to 9.180	5.8976	-0.71	9	0.4983
Solar KWH1	4.266	-23.829	to 32.361	12.4197	0.34	9	0.7391
Wind KWH1	4.449	-11.391	to 20.289	7.0021	0.64	9	0.5410

Source of variation	Sum squares	DF	Mean square	F statistic	p
Model	0.964	16	0.060	6.64	0.0034
Residual	0.082	9	0.009
Total	1.046	25

Coefficients	Coefficient Value	Value	Temperature	Ave	Temperature
Intercept	-71.98	1.00E+00	-7.20E+01	1.00E+00	-71.98
Coal KWH1	9.077	1.48E+00	1.34E+01	9.00E-01	8.1693
NG KWH1	2.671	4.71E+00	1.26E+01	3.10E+00	8.2801
Oil KWH1	4.071	1.85E+00	7.53E+00	1.15E+00	4.68165
NP KWH1	21.33	2.30E-01	4.91E+00	2.30E-01	4.9059
Hydro KWH1	3.985	3.67E-01	1.46E+00	3.67E-01	1.462495
Geo KWH1	-4.161	9.20E-02	-3.83E-01	9.20E-02	-0.382812
Solar KWh1	4.266	4.17E-02	1.78E-01	5.00E-01	2.133
Wind KWH1	4.449	1.43E-01	6.36E-01	1.40E+00	6.2286
CO2 GDP	0.005277	5.10E+02	2.69E+00	4.80E+02	2.53296
CO2 GDP1	-0.0009683	4.16E+02	-4.03E-01	3.90E+02	-0.377637
USP	-5.521	1.80E-01	-9.94E-01	1.60E-01	-0.88336
Energy Cost	-0.2749	1.00E+01	-2.75E+00	1.00E+01	-2.749
Biomass KWH1	2.388	3.21E-01	7.67E-01	3.21E-01	0.766548
Energy ConsP	2.05E-08	9.46E+07	1.94E+00	5.50E+07	1.12893
CO2	0.0813	3.90E+02	3.17E+01	4.30E+02	34.959
Temp5	-1.013	6.50E-01	-6.58E-01	7.00E-01	-0.7091
Result			6.68E-01		-1.833426

The model has excellent correlation as shown by the R² variable equal to 0.92. The results indicate that the 2009 value for global temperatures can decrease from 0.7 degrees to -1.8 degrees by decreasing the United States reliance on coal, natural gas, and oil by 25% and increasing the United States reliance on Solar and Wind energy by 25% (highlighted in red). Nuclear power, hydro power, biomass, and geothermal variables were held constant. Other 2009 variables were adjusted to keep up with the trends of using renewable energies such as increased energy costs, higher global temperatures (Temp5 – even with lower CO2 emissions the global temperature trend is upward), lower energy consumption (including the USP variable), and lower CO2 to GDP numbers.

Global warming alarmist may look at this as proof that moving to renewable energy sources will eliminate CO2 and therefore; reduce global temperatures. This, however, is not true and this result can be misleading as will be revealed in future blog posts evaluating this model.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumption (Part II)

2011-11-15T06:06:00.000-08:00

Below is the result of running a linear regression analysis on the model posted in Part I of this series of blogs solving for energy consumption.

R²

1.00

Adjusted R²

0.99

666,086.3

Term

Coefficient

95% CI

t statistic

Intercept

378255947

-226719665

to 983231560

267433091

1.41

0.1909

Temp

1003424

-4214167

to 6221015

2306467

0.44

0.6738

Temp5

14160839

972170

to 27349508

5830130

2.43

0.0381

CO2

100280

-707089

to 907649

356902

0.28

0.7851

Energy Cost

846385

-2231455

to 3924225

1360578

0.62

0.5493

USP

35774485

-96506874

to 168055844

58475767

0.61

0.5558

CO2 GDP

-9505

-96114

to 77105

38286.4

-0.25

0.8095

CO2 GDP1

25085

-47297

to 97466

31997

0.78

0.4532

Coal KWH1

-66478559

-165573816

to 32616698

43805652

-1.52

0.1634

NG KWH1

-21325244

-54839198

to 12188711

14815044

-1.44

0.1839

Oil KWH1

-42947504

-87151923

to 1256915

19540828

-2.20

0.0555

NP KWH1

-176333962

-469778220

to 117110296

129718791

-1.36

0.2071

BIO KWH1

-8995579

-103871161

to 85880002

41940319

-0.21

0.8349

Hydro KWH1

-32454908

-87709284

to 22799468

24425528

-1.33

0.2166

Geo KWH1

-10174301

-105691745

to 85343143

42224058

-0.24

0.8150

Solar KWH1

-79532965

-267937269

to 108871338

83285250

-0.95

0.3645

Wind KWH1

-109678477

-186980783

to -32376172

34171947

-3.21

0.0107

Source of variation	Sum squares	DF	Mean square	F statistic	p
Model	###########	16	###########	238.14	<0.0001
Residual	###########	9	###########
Total	###########	25

Coefficients	Coefficient Value	Value	Energy Consumption	Ave	Energy Consumption
Intercept	378255947	1.00E+00	3.78E+08		1.00E+00	378255947
Coal KWH1	-66478559	1.48E+00	-9.84E+07		9.00E-01	-59830703.1
NG KWH1	-21325244	4.71E+00	-1.00E+08		3.10E+00	-66108256.4
Oil KWH1	-42947504	1.85E+00	-7.95E+07		1.15E+00	-49389629.6
NP KWH1	-176333962	2.30E-01	-4.06E+07		2.30E-01	-40556811.26
Hydro KWH1	-32454908	3.67E-01	-1.19E+07		3.67E-01	-11910951.24
Geo KWH1	-10174301	9.20E-02	-9.36E+05		9.20E-02	-936035.692
Solar KWh1	-79532965	4.17E-02	-3.32E+06		5.00E-01	-39766482.5
Wind KWH1	-109678477	1.43E-01	-1.57E+07		1.40E+00	-153549867.8
CO2 GDP	-9505	5.10E+02	-4.85E+06		4.80E+02	-4562400
CO2 GDP1	25085	4.16E+02	1.04E+07		3.90E+02	9783150
USP	35774485	1.80E-01	6.44E+06		1.60E-01	5723917.6
Temp	1003424	7.00E-01	7.02E+05		7.00E-01	702396.8
Biomass KWH1	-8995579	3.21E-01	-2.89E+06		3.21E-01	-2887580.859
Energy Cost	8.46E+05	1.00E+01	8.46E+06		1.30E+01	11003005
CO2	100280	3.90E+02	3.91E+07		4.30E+02	43120400
Temp5	14160839	6.50E-01	9.20E+06		6.50E-01	9204545.35
Result			9.42E+07			28294643.3

The model has excellent correlation as shown by the R² variable equal to 1. The results indicate that the 2009 value for energy consumption of 9.42E7 billion BTUs can go down to 2.83E7 BTUs by decreasing the United States reliance on coal, natural gas, and oil by 25% and increasing the United States reliance on Solar and Wind energy by 25% (highlighted in red). Nuclear power, hydro power, biomass, and geothermal variables were held constant. Other 2009 variables were adjusted to keep up with the trends of using renewable energies such as increased energy costs, higher global temperatures (Temp and Temp5 – even with reduced CO2 emissions the temp trend is upward), lower energy consumption (including the USP variable), and lower CO2 to GDP numbers.

Global warming alarmist may point out that reduced energy consumption is due to renewable energies, but this is not true. The trend for energy consumption has been downward in the United States for several years because of its expense and the introduction of more efficient and lower power appliances.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

The Relationship Between Energy Costs, Global Temperatures, CO2, and Energy Consumptions (Part I)

2011-11-09T06:47:00.000-08:00

What is the relationship between energy costs, global temperatures, carbon dioxide (CO2) emissions, and energy consumption? They are not only closely related, they correlate almost perfectly. Data for all four classifications (energy costs, global temperatures, CO2 emissions, and energy consumption) was evaluated over the past 26 years (1984 – 2009 – this is the last year data was available). Each classification (energy costs, global temperatures, CO2 emissions, and energy consumption) was evaluated using the cost and usage of various energy sources: coal, natural gas, oil, hydro, solar, nuclear, wind, biomass and geothermal. This data was obtained from multiple sources including the Department of Energy (U.S. energy information page) and from a few climate change sites that report global temperatures and CO2 levels in the atmosphere. A linear error model was created to evaluate the data to see its impact on energy costs, global temperatures, CO2 emissions, and energy consumption. To understand how to create and evaluate a linear error model please refer to my blogs: Creating an Election Model Part I, Part II, and Part III on October 4, 5, and 6 2010. Below is the data used in the linear error model:

Year	Temp	Temp5	CO2	Energy Cost	USP	Energy Cons	CO2 GDP	CO2 GDP1	Coal KWH1	NG KWH1	Oil KWH1	NP KWH1	BIO KWH1	Hydro KWH1	Geo KWH1	Solar KWH1	Wind KWH1
1984	0.13	0.16	344.42	6.25	0.241588	76,713,905	830	701	1.4305119	4.1803792	3.2815861	0.103086	0.2586444	0.491240922	0.04306401	2.2344E-05	1.4799E-05
1985	0.12	0.22	345.9	6.44	0.235288	76,490,692	820	672	1.4251773	4.0072186	3.3615293	0.1202246	0.2669266	0.438086805	0.05264189	4.5841E-05	1.32745E-05
1986	0.18	0.24	347.15	6.44	0.230906	76,756,373	810	650	1.452073	3.9395768	3.2824795	0.1304031	0.2618799	0.457170483	0.05872764	6.127E-05	9.82465E-06
1987	0.34	0.26	348.93	6.37	0.232802	79,172,664	800	651	1.4884201	4.052258	3.1347853	0.1405256	0.2549434	0.389378318	0.06095451	4.5108E-05	8.20286E-06
1988	0.41	0.33	351.48	6.35	0.235984	82,819,365	790	654	1.5020819	4.079081	3.0037374	0.1618715	0.2621525	0.338153948	0.05666	3.8129E-05	1.95568E-06
1989	0.27	0.38	352.91	6.45	0.235905	84,943,729	780	643	1.5372354	4.1101475	2.7837345	0.1612691	0.2728448	0.408380604	0.08217137	0.02228322	0.00475697
1990	0.47	0.34	354.19	6.57	0.231887	84,651,424	770	625	1.5865355	4.1374145	2.6365779	0.172269	0.2315601	0.429856343	0.08528884	0.02359397	0.006139483
1991	0.42	0.29	355.59	6.75	0.231098	84,606,296	766	621	1.5338165	4.1351803	2.6712938	0.1821072	0.2366436	0.427603296	0.08836422	0.0248863	0.00654943
1992	0.14	0.3	356.37	6.82	0.236123	85,955,467	755	612	1.546787	4.1924503	2.604929	0.1847864	0.2508336	0.373245257	0.08966037	0.02550833	0.006387677
1993	0.17	0.29	357.04	6.93	0.238454	87,601,399	745	607	1.4844886	4.3411896	2.5393931	0.1871848	0.2547774	0.422171471	0.09558365	0.02716777	0.006786088
1994	0.3	0.28	358.88	6.91	0.240162	89,256,869	729	592	1.5659371	4.3668369	2.3872056	0.1888506	0.2562424	0.378534297	0.08584965	0.02707469	0.007524257
1995	0.44	0.33	360.88	6.89	0.238916	91,169,286	717	583	1.5515063	4.2811497	2.3366462	0.198424	0.2607326	0.449449185	0.07417759	0.02742704	0.006863084
1996	0.36	0.43	362.84	6.86	0.241701	94,172,227	710	582	1.5687341	4.2609308	2.2670406	0.1951213	0.2606299	0.494179841	0.0781887	0.02730397	0.006905414
1997	0.38	0.46	363.76	6.85	0.241752	94,761,405	686	564	1.6046652	4.2723039	2.2565676	0.1816583	0.2567431	0.501228068	0.08053422	0.02707679	0.006935284
1998	0.7	0.45	366.63	6.74	0.245347	95,178,462	659	545	1.6461016	4.296635	2.1745415	0.193541	0.2406126	0.451423654	0.08091063	0.02675413	0.006336463
1999	0.43	0.49	368.31	6.64	0.248991	96,812,431	633	526	1.6198953	4.3037291	2.0779723	0.211681	0.2474275	0.454441761	0.08284129	0.0267889	0.009574141
2000	0.41	0.55	369.48	6.81	0.239983	98,969,664	624	521	1.5902187	4.4006801	2.0745111	0.2199708	0.2522749	0.393243486	0.07976915	0.02600341	0.011972448
2001	0.56	0.55	371.02	7.29	0.240135	96,316,206	607	506	1.6378856	4.4885591	2.0502718	0.2233881	0.2190373	0.311877905	0.07794314	0.02549592	0.014527239
2002	0.68	0.58	373.1	7.2	0.236797	97,853,419	602	501	1.6024089	4.3848167	2.057759	0.2296704	0.228847	0.379100808	0.08331345	0.02541817	0.022275168
2003	0.65	0.65	375.64	7.44	0.229022	98,130,959	592	493	1.5736934	4.47503	2.0558244	0.2267584	0.2397917	0.40237339	0.08476128	0.02537665	0.024482094
2004	0.59	0.67	377.38	7.61	0.218337	100,313,215	583	485	1.6241176	4.3379936	1.9620924	0.2337373	0.2556507	0.382372132	0.08726753	0.02567078	0.030222656
2005	0.77	0.68	379.97	8.14	0.207232	100,444,707	568	473	1.6657972	4.2662489	1.8903268	0.2345334	0.2675885	0.388399099	0.08860759	0.02660753	0.038385518
2006	0.65	0.66	381.88	8.9	0.200377	99,789,632	547	454	1.676328	4.2891668	1.8266393	0.2315597	0.2727646	0.404333136	0.08697873	0.02849909	0.055752899
2007	0.75	0.68	384	9.13	0.19661	101,527,089	544	452	1.6403719	4.4296163	1.7966175	0.236157	0.2923386	0.341636389	0.08765979	0.03165016	0.071326483
2008	0.55	0.65	387	9.74	0.19	99,401,914	529	436	1.6242809	4.5401069	1.7175183	0.22956	0.3160297	0.342013483	0.08836366	0.03686118	0.111455643
2009P	0.72	0.7	391	9.89	0.180645	94,578,267	510	416	1.4785753	4.7142289	1.8486182	0.2288414	0.3206854	0.36749038	0.09209944	0.04165733	0.143335169

Where Temp and Temp5 are the annual and 5 year average of global temperature increases over the 1950 to 1980 average global temperatures respectively; CO2 is the annual increase in CO2 emissions in the earth’s atmosphere in parts per million; Energy Cost is the average annual cost for electricity in kilowatt – hours; USP is the percentage of the annual contribution of global emissions by the United States; Energy Cons is the annual energy consumption by the United States in billions of British Thermal Units (BTUs – 3,412 BTUs is equal to 1 kilowatt-hour of electricity); CO2 GDP and CO2 GDP1 are equal to the United States carbon emissions output to produce one dollar of Gross Domestic Product (GDP) revenue; and Coal KWH1, NG KWH1, Oil KWH1, NP KWH1, Bio KWH1, Hydro KWH1, Geo KWH1, Solar KWH1, and Wind KWH1 are the annual cost and percentage of use of coal, natural gas, oil, nuclear power, biomass, hydro power, geothermal, solar, and wind energy sources. In the next part of this series of blogs a linear regression analysis on the above model will be completed to solve for energy consumption.

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The Relatiohship Between Healthcare Costs and Good Health (Part VI)

2011-09-27T06:21:00.001-07:00

To summarize the results of the running a linear regression analysis on the model shown in Part I of this series of blogs are as follows:

Variable 2009 Result ObamaCare Projection
--------------------------------------------------------------------------------------------------------------------
Healthcare Insurance Premium $13,375 $14900
Doctors Per Capita (per 1000 people) 2.4 2
Nurses Per Capita (per 1000 people) 9.7 8.1
Life Expectancy 78 76.2
Obesity 28% 32.7%
Healthcare % of GDP 16.2% 16.8%
Healthcare Costs Per Capita $7700 $9000

These results are the consequence of moving 30 million uninsured Americans into government run insurance plans such as Medicaid. The results indicate that there will be a 17% rationing of doctors; a 17% rationing of nurses; an 12% increase in insurance premiums; a 1.8 year reduction in the life expectancy rate; a 4.2% increase in the national obesity rate; a 0.6% or 80 billion dollar annual increase in the size of the healthcare industry compared to our gross domestic product (This may not sound like much, but the United States annual GDP grows about 3% per year or 400 billion dollars annually. Thus, the annual United States medical industry is growing by nearly 500 billion dollars!); and finally there will be a 17% increase in the annual healthcare expenditures per person.

The bottom line; any government attempt to take over a large portion of the healthcare industry will result in much higher expenses for every American while at the same time risking the wellbeing of every American. This is a lose – lose scenario. Some may argue the claims in this model are false, but it does accurately follow what happened when the state of Massachusetts implemented a similar law a few years back. It also tracks the trends of other socialized healthcare systems around the globe (i.e. Great Britain and Canada). Thus, the fears of many Americans that ObamaCare will lead to higher costs, shoddier care, and rationing are all well founded.

Since the ObamaCare law is so complex, it is very difficult to predict how these variables will actually trend. However, there is nothing wrong with looking at present healthcare trends to forecast the potential risks of creating a massive government run healthcare program. I seriously doubt anyone took the time to evaluate these important trends prior to developing and implementing the ObamaCare legislation.

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The Relationship Between Healthcare Costs and Good Health (Part V)

2011-09-24T07:51:00.000-07:00

Below are the results of running a linear regression analysis on the model posted in Part I of this series of blogs solving for No Ins (the number Americans without insurance), Gov Total (the number of Americans with government health insurance plans), and Priv Total (the number of Americans with private health insurance plans). The units for all three categories (No Ins, Priv Total, and Gov Total) are in thousands of people.

No Insurance

R2 1
SE 225.4

Coefficient Value 2009 No Insurance Projection No Insurance
Intercept 143447.00000 1 143447 1 143447
Priv Total -0.60350 194545 -117407.9075 194545 -117407.9075
Gov Total -0.86580 93167 -80663.9886 124000 -107359.2
Costs GDP 7867.00000 16.2 127445.4 17 133739
Expense Per Capita -37.06000 7700 -285362 9000 -333540
Doctors Per Capita 24614.00000 2.4 59073.6 2 49228
Health Ins Rate 18.21000 13375 243558.75 15000 273150
Obesity 63.97000 28 1791.16 33 2111.01
Nurses Per Capita -13362.00000 9.7 -129611.4 8 -106896
Life Expectancy 1134.00000 78 88452 76 86184

Result 50722.6139 22655.9025

Government Insured

R2 1
SE 258.3

Coefficient Value 2009 Government Insured Projection Government Insured
Intercept 159489.0000 1 159489 1 159489
Priv Total -0.6906 194545 -134352.777 194545 -134352.777
No Ins -1.1370 50674 -57616.338 20000 -22740
Costs GDP 9095.0000 16.2 147339 17 154615
Expense Per Capita -42.7800 7700 -329406 9000 -385020
Doctors Per Capita 28303.0000 2.4 67927.2 2 56606
Health Ins Rate 20.9900 13375 280741.25 15000 314850
Obesity 70.6200 28 1977.36 31 2189.22
Nurses Per Capita -15426.0000 9.7 -149632.2 8 -123408
Life Expectancy 1368.0000 78 106704 76 103968

Result 93170.495 126196.443

Private Insured

R2 1
SE 258.3

Coefficient Value 2009 Private Insured Projection Private Insured
Intercept 125153.0000 1 125153 1 125153
Gov Total -1.2930 93167 -120464.931 124000 -160332
No Ins -1.4890 50674 -75453.586 20000 -29780
Costs GDP 10651.0000 16.2 172546.2 17 181067
Expense Per Capita -48.9800 7700 -377146 9000 -440820
Doctors Per Capita 26540.0000 2.4 63696 2 53080
Health Ins Rate 23.8200 13375 318592.5 15000 357300
Obesity 547.4000 28 15327.2 33 18064.2
Nurses Per Capita -16762.0000 9.7 -162591.4 8 -134096
Life Expectancy 3008.0000 78 234624 76 228608

Result 194282.983 198244.2

The results from all three linear regression analyses above illustrate very good correlation because the R² variable is equal to 1. These results were computed to check the validity of the prior analyses done in Parts II, III, and IV of this series of blogs since the expected number of people with no insurance is to decline from 50 million to 20 million; the number of people with government insurance plans is expected to increase from 94 million to 124 million; and the number of people with private insurance plans is expected to stay the same at 194 million. For the most part these results correlate since No Ins dropped to 22 million, Gov Total increased to 126 million, and Priv Total increased to 198 million. These results are within 4% of what was expected and the difference can be easily explained by the upward population growth trend in the model. All of the results in Part I through Part V of this blog series on healthcare will be summarized in Part VI.

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The Relationship Between Healthcare Costs and Good Health (Part IV)

2011-09-21T07:20:00.001-07:00

Below are the results of running a linear regression analysis on the model posted in Part I of this series of blogs solving for Obesity (percentage of Americans with a body mass index over 30), Costs GDP (the annual health industry market size as a percentage of GDP), and Exp Per Capita (the average annual healthcare expenditure per person):

Obesity

R2 1
SE 0.22

Coefficient Value 2009 Obesity Percent Projection Obesity Percent
Intercept 174.000000000 1 174 1 174
Priv Total 0.000217900 194545 42.3913555 194545 42.3913555
Gov Total 0.000052627 93167 4.903099709 124000 6.525748
No Ins 0.000062590 50674 3.17168566 20000 1.2518
Costs GDP 1.504000000 16.2 24.3648 17 25.568
Exp Per Capita -0.009257000 7700 -71.2789 9000 -83.313
Health Ins Rate 0.005210000 13375 69.68375 15000 78.15
Life Expectancy -2.741000000 78 -213.798 76 -208.316
Doctors Per Capita 19.380000000 2.4 46.512 2 38.76
Nurses Per Capita -5.351000000 9.7 -51.9047 8 -42.808

Result 28.04509087 32.2099035

Cost Per GDP

R2 1
SE 0.03

Coefficient Value 2009 Costs Per GDP Projection Costs Per GDP
Intercept -21.490000 1 -21.49 1 -21.49
Priv Total 0.000064 194545 12.36605838 194545 12.36605838
Gov Total 0.000102 93167 9.4564505 124000 12.586
No Ins 0.000115 50674 5.8477796 20000 2.308
Expense Per Capita 0.004766 7700 36.6982 9000 42.894
Doctors Per Capita -3.524000 2.4 -8.4576 2 -7.048
Health Ins Rate -0.002351 13375 -31.444625 15000 -35.265
Obesity 0.022540 28 0.63112 33 0.74382
Nurses Per Capita 1.778000 9.7 17.2466 8 14.224
Life Expectancy -0.059710 78 -4.65738 76 -4.53796

Result 16.19660348 16.78091838

Expense Per Capita

R2 1
SE 0.007

Coefficient Value 2009 Expense Per Capita Projection Expense Per Capita
Intercept 4672.0000 1 4672 1 4672
Priv Total -0.0128 194545 -2484.33965 194545 -2484.33965
Gov Total -0.0209 93167 -1944.39529 124000 -2587.88
No Ins -0.0237 50674 -1203.00076 20000 -474.8
Costs GDP 208.1000 16.2 3371.22 17 3537.7
Doctors Per Capita 762.7000 2.4 1830.48 2 1525.4
Health Ins Rate 0.4942 13375 6609.925 15000 7413
Obesity -6.0600 28 -169.68 33 -199.98
Nurses Per Capita -375.0000 9.7 -3637.5 8 -3000
Life Expectancy 8.3980 78 655.044 76 638.248

Result 7699.7533 9039.34835

The above model results have excellent correlation as illustrated by the R² variable equal to 1. The Obesity rate will increase from 28% to 32.2%, the healthcare industry will grow from 16.2% to 16.8% of GDP, and the average annual healthcare expense per person will increase from 7700 to 9000 dollars by moving 30 million Americans with no healthcare insurance to a government run healthcare insurance plan. It has already been determined that Exp Per Capita (the average annual healthcare expenditures per capita), Costs GDP (the healthcare industry market size as a percent of GDP), Health Ins Rate (the annual family health insurance premium), and the Obesity rate will all increase using this model. On the other hand, it was determined that the Life Expectancy, the Doctors Per Capita, and Nurses Per Capita will all go down. Those numbers are updated in the projection column in the above table.

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The Relationship Between Healthcare Costs and Good Health (Part III)

2011-09-14T06:09:00.001-07:00

Below are the results of running a linear regression analysis on the model posted in Part I of this series of blogs solving for Doctors Per Capita (the average number of doctors per 1000 people), Nurses Per Capita (the average number of nurses per 1000 people), and Life Expectancy (the average life span for Americans):

Doctors Per Capita

R2 1
SE 0.007

Coefficient Value 2009 Doctors Per Capita Projection Doctors Per Capita
Intercept -6.69700000 1 -6.697 1 -6.697
Priv Total 0.00000987 194545 1.919633879 194545 1.919633879
Gov Total 0.00001970 93167 1.8353899 124000 2.4428
No Ins 0.00002249 50674 1.139708934 20000 0.44982
Costs GDP -0.21950000 16.2 -3.5559 17 -3.7315
Exp Per Capita 0.00108800 7700 8.3776 9000 9.792
Health Ins Rate -0.00054610 13375 -7.3040875 15000 -8.1915
Obesity 0.01810000 28 0.5068 33 0.5973
Nurses Per Capita 0.43080000 9.7 4.17876 8 3.4464
Life Expectancy 0.02561000 78 1.99758 76 1.94636

Result 2.398485213 1.974313879

Nurses Per Capita

R2 1
SE 0.02

Coefficient Value 2009 Nurses Per Capita Projection Nurses Per Capita
Intercept 13.630000 1 13.63 1 13.63
Priv Total -0.000030 194545 -5.928369785 194545 -5.928369785
Gov Total -0.000052 93167 -4.890708498 124000 -6.509256
No Ins -0.000060 50674 -3.0252378 20000 -1.194
Costs GDP 0.541500 16.2 8.7723 17 9.2055
Exp Per Capita -0.002616 7700 -20.1432 9000 -23.544
Health Ins Rate 0.001300 13375 17.3875 15000 19.5
Obesity -0.024430 28 -0.68404 33 -0.80619
Doctors Per Capita 2.107000 2.4 5.0568 2 4.214
Life Expectancy -0.005975 78 -0.46605 76 -0.4541

Result 9.708993917 8.113584215

Life Expectancy

R2 1
SE 0.07

Coefficient Value 2009 Life Expectancy Projection Life Expectancy
Intercept 45.6900000 1 45.69 1 45.69
Priv Total 0.0001132 194545 22.022494 194545 22.022494
Gov Total 0.0000964 93167 8.982789472 124000 11.955584
No Ins 0.0001049 50674 5.3157026 20000 2.098
Costs GDP -0.3766000 16.2 -6.10092 17 -6.4022
Exp Per Capita 0.0012130 7700 9.3401 9000 10.917
Health Ins Rate -0.0003751 13375 -5.0169625 15000 -5.6265
Obesity -0.2591000 28 -7.2548 33 -8.5503
Doctors Per Capita 2.5920000 2.4 6.2208 2 5.184
Nurses Per Capita -0.1237000 9.7 -1.19989 8 -0.9896

Result 77.99931357 76.298478

The above model results have excellent correlation as illustrated by the R² variable equal to 1. The doctors per capita will decrease from 2.4 to 2, the nurses per capita will decrease from 9.7 to 8.1, and the life expectancy will decrease from 78 to 76.2 years old by moving 30 million Americans with no healthcare insurance to government run healthcare insurance plans. It has already been determined that Exp Per Capita (the annual average healthcare expenditures per capita), Costs GDP (the healthcare industry size as a percent of GDP), Health Ins Rate (the annual family healthcare insurance premium rate), and the Obesity rate will increase in this model. On the other hand, it was determined that the Life Expectancy, the Doctors Per Capita, and Nurses Per Capita will all go down. Those numbers are updated in the projection column in the above table.

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The Relationship Between Healthcare Costs and Good Health (Part II)

2011-09-07T06:52:00.001-07:00

Below is the result of running a linear regression analysis on the model posted in Part I of this series of blogs solving for Health Ins Rate (the average annual health insurance rate for American families):

n 11

R2 1.00
Adjusted R2 1.00
SE 11.5

Term Coefficient 95% CI SE t statistic DF p
Intercept -9818 -121313 to 101678 8774.9 -1.12 1 0.4643
Priv Total 0.02536 -0.23553 to 0.28625 0.020532 1.24 1 0.4333
Gov Total 0.04183 -0.15634 to 0.24000 0.015596 2.68 1 0.2272
No Ins 0.04766 -0.18862 to 0.28393 0.018595 2.56 1 0.2368
Costs GDP -419.5 -1044.8 to 205.7 49.21 -8.53 1 0.0743
Exp Per Capita 2.019 0.853 to 3.185 0.0918 22.01 1 0.0289
Doctors Per Capita -1564 -9780 to 6652 646.6 -2.42 1 0.2496
Nurses Per Capita 761.2 -247.6 to 1769.9 79.39 9.59 1 0.0662
Life Expectancy -10.61 -2143.47 to 2122.25 167.860 -0.06 1 0.9598
Obesity 13.93 -618.89 to 646.75 49.804 0.28 1 0.8263

Source of variation Sum squares DF Mean square F statistic p
Model 67361655.0 9 7484628.3 56285.47 0.0033
Residual 133.0 1 133.0
Total 67361788.0 10

Coefficient Value 2009 Health Insurance Rate Projection Health Insurance Rate
Intercept -9818 1 -9818 1 -9818
Priv Total 0.02536 194545 4933.6612 194545 4933.6612
Gov Total 0.04183 93167 3897.17561 124000 5186.92
No Ins 0.04766 50674 2415.12284 20000 953.2
Costs GDP -419.5 16.2 -6795.9 17 -7131.5
Exp Per Capita 2.019 7700 15546.3 9000 18171
Obesity 13.93 28 390.04 33 459.69
Life Expectancy -10.61 78 -827.58 76 -806.36
Doctors Per Capita -1564 2.4 -3753.6 2 -3128
Nurses Per Capita 761.2 9.7 7383.64 8 6089.6

Result 13370.85965 14910.2112

The above model has excellent correlation as illustrated by the R² variable equal to 1. The results indicate the annual health insurance rate will go up by over 1500 dollars annually by moving 30 million people with no health insurance to a government run health insurance plan. It has already been determined that the Exp Per Capita (annual average healthcare expenditure per capita), Costs GDP (healthcare costs as a percent of GDP), and the Obesity rate will all go up in this model. On the other hand, it was determined that the Life Expectancy, the Doctors Per Capita, and Nurses Per Capita will all go down. Those numbers are updated in the projection column in the above table.

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The Relationship Between Healthcare Costs and Good Health

2011-09-05T07:22:00.001-07:00

What is the relationship between healthcare costs and good health? They are not only closely related, they correlate perfectly. Data was broken down into 10 classifications and was evaluated over the past 11 years (1999 – 2009 – there was not much data on the subject earlier than 1999). This data was obtained from multiple sites including the Bureau of Economic Analysis, the Bureau of Labor Statistics, the Census Bureau, and the Organization for Economic Cooperation and Development . A linear error model was created to evaluate the data to see the impact of moving millions of uninsured persons into government run healthcare (this is an attempt to model the potential outcome of the recently passed ObamaCare legislation). To understand how to create and evaluate a linear error model please refer to my blogs: Creating an Election Model Part I, Part II, and Part III on Oct 4, 5, and 6 2010. Below is the data used in the linear error model:

Year Priv Total Gov Total No Ins Costs GDP Exp Per Capita Doctors Per Capita Nurses Per Capita Life Expectancy Obesity Health Ins Rate
2009 194,545 93,167 50,674 16.2 7,700 2.40 9.7 78 28 13375
2008 200,992 87,411 46,340 16.0 7,538 2.40 9.5 78 27.5 12680
2007 201,991 83,031 45,657 15.7 7,285 2.40 9.5 77.9 26.6 12106
2006 201,690 80,270 46,995 15.5 6,931 2.40 9.6 77.7 26.2 11480
2005 201,167 80,213 44,815 15.4 6,563 2.40 9.9 77.4 25.3 10880
2004 200,924 79,486 43,498 15.4 6,196 2.40 9.8 77.4 24.3 9950
2003 199,871 76,755 43,404 15.3 5,852 2.40 9.7 77.1 23.5 9068
2002 200,891 73,624 42,019 14.8 5,453 2.40 9.3 76.9 23.8 8003
2001 201,695 71,295 39,760 14.1 5,052 2.40 9.0 76.9 22.9 7061
2000 202,794 69,037 38,426 13.4 4,703 2.30 8.7 76.8 21.8 6438
1999 200,721 67,683 38,767 13.2 4,500 2.30 8.7 76.7 21 6000

Where Priv Total equals the number of Americans who own private healthcare insurance plans (in thousands of people); Gov Total equals the number of Americans who own government healthcare insurance plans (in thousands of people); No Ins equals the number of Americans who do not have any healthcare insurance (in thousands of people); Costs GDP equals the overall costs of running the healthcare industry as a percentage of the United States Gross Domestic Product (GDP); Exp Per Capita equals the annual expenditures for Americans on healthcare (in dollars); Doctors Per Capita equals the number of doctors per 1000 people; Nurses per Capita equals the number of nurses per 1000 people; Life Expectancy is average age of the American life span; Obesity is the percent of Americans who have a body mass index greater than 30; and Health Ins Rate is the average annual expense American families spends on health insurance premiums.

In Part II of this post we will begin to run various linear regression analyses on the above model to see the potential trends of implementing the ObamaCare legislation.

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Economic Model Summary

2011-08-30T06:19:00.001-07:00

What was learned from the economic models posted over the past several months?

• If the federal government raises taxes and receives more tax revenue it spends more money because the debt, budget and government spending continue to increase.

• Maximizing consumer spending does just as much to reduce the poverty level as increased government spending and debt.

• Government Social Benefits needs to be minimized. This economic parameter had the most significant negative impact on other economic parameters.

• Government budget deficits and debt need to be minimized. This economic parameter had the second most significant negative impact on other economic parameters.

• Population increases had little impact on economic parameters.

• State budget deficits and state social benefit payments had little overall impact on national economic parameters.

• Consumer spending needs to be maximized. This parameter had the most significant positive impact on other economic parameters.

• Personal income needs to be maximized. This parameter has the second most significant positive impact on other economic parameters.

• The trade deficit has a big impact on many economic indicators (some good and some bad).

• Economic parameters such as GDP, tax receipts, unemployment, inflation, and government spending had less of an impact on other economic parameters than one may suspect.

• Government spending is a leading cause for inflation and driving energy prices and healthcare costs higher.

These are just a few of the obvious observations. I will also post models in the future on other complex issues / problems including healthcare and climate change.

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Summary: The Relationship Between Taxes, Income, Debt, Spending, Social Benefits, and the Trade Deficit

2011-08-29T06:17:00.000-07:00

This is summary of linear regression model results (posted over the past several weeks) on the relationship between taxes, income, debt, spending, social benefits, and the trade deficit. The following results are dependent on changing the fifth quintile’s effective tax rate from 13 to 17.5%, its income from 46,800 to 50,800 dollars, and its consumer spending rate from 36 to 40%. This models the result if the highest income bracket tax rate is increased from 35% to 39.5%.

• Federal government Tax Revenue will vary from 1.09 trillion to 1.26 trillion dollars. The model accuracy is perfect (R² = 1).
• Consumer Spending will vary from 12.2 trillion to 12.9 trillion dollars. The model accuracy is perfect (R² = 1).
• The federal government Effective Tax Rate will vary from 8.3% to 11.2%. The model accuracy is perfect (R² = 1).
• Federal government expenditures on Social Benefit spending will vary from 1.74 trillion to 2.13 trillion dollars. The model accuracy is near perfect (R² = 0.97).
• The national Debt will vary from 9.6 trillion to 11.5 trillion dollars. The model accuracy is perfect (R² = 1).
• The national Trade Deficit will vary from a loss of 441 billion to 813 billion dollars annually. The model accuracy is perfect (R² = 1). The trend results are similar if the trade deficit is adjusted for oil imports.

Similar results are achieved from another model that varies the effective tax rate from 8% to 11%, the first quintiles adjusted social benefits income from 17,800 dollars to 21,800 dollars, and the fifth quintiles adjusted social benefits income from 46,800 dollars to 50,800 dollars.

• Federal government expenditures on Social Benefit spending will vary from 1.8 trillion to 2.03 trillion dollars. The model accuracy is near perfect (R² = .97).
• The national Debt will vary from 9.15 trillion to 10.95 trillion dollars. The model accuracy is near perfect (R² = .99).
• The national Trade Deficit will vary from a loss of 725 billion to 881 billion dollars. The model accuracy is near perfect (R² = .98). The trend results are similar if the trade deficit is adjusted for oil imports.

Since consumer spending, income, and the tax rate are directly related – it makes sense to use all three when computing the tax rate. An example of a fair tax rate formula is: Tax Rate (TR) = Income (I) / (1 / (1 – Consumer Spending (CS))). After all, if the tax rate goes up and consumer spending goes down, it will adversely affect the economy.

Summary: If the federal government raises the income tax on the highest earners (fifth quintile) from 35% to 39.6%: tax revenues will go up nearly 170 billion dollars annually; consumer spending will go down 700 billion dollars; the effective federal income tax rate will go up from 8.3 to 11.2%; the federal government spending on social benefits will increase by 300 billion dollars; and the national debt will sore 1.9 trillion dollars. These results track if a separate model is created taking into account adjusted national incomes due to social benefits (entitlement spending). The bottom line – increasing taxes on the highest earners will only create higher social benefit spending, a much greater national debt, and much lower consumer spending that could cost the economy up to 2 million jobs. In other words, raising taxes is the main ingredient in a formula to create a recession. On the hand, there is an argument that increased taxes could both increase or decrease the trade deficit.

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Advanced Economic Data

2011-08-26T06:47:00.001-07:00

The economic data in my last post included the effects of inflation, unemployment, the budget, debt, and the gross domestic product on our economy. The below data adds the effects of consumer spending on the results from 1930 to 2010 from yesterday’s data. The last two columns add the effects of housing and auto sales as well as the trade surplus/deficit numbers on the economy from 1960 to 2010. Each of the annual results is ranked in order from best to worst.

Year Administration Administration Rank Year Rank Year Result Year Result Year Rank
Lower is Better Higher is Better Higher is Better
Included Trade Deficit/Housing/Auto
1930 Hoover 67.5 71 0.139345797
1931 64 0.210419081
1932 FDR 62.53846154 74 0.105261784
1933 77 0.065927047
1934 78 0.055874251
1935 75 0.089663178
1936 72 0.12937271
1937 73 0.112178013
1938 60 0.284495172
1939 59 0.292502039
1940 53 0.338303809
1941 61 0.243776477
1942 69 0.173640032
1943 47 0.404796287
1944 15 0.838565937
1945 Truman 72.57142857 65 0.201738546
1946 81 0.013678973
1947 80 0.01763889
1948 79 0.039130755
1949 67 0.190822755
1950 66 0.197421359
1951 70 0.153934325
1952 12 0.877539585
1953 Eisenhower 16.125 1 0.995273461
1954 25 0.682557344
1955 2 0.987840742
1956 11 0.886158024
1957 24 0.68423737
1958 40 0.454862223
1959 10 0.904730878
1960 16 0.806800956 0.7849878 10
1961 JFK 11 17 0.779490145 0.743358727 13
1962 7 0.92143518 0.932567101 6
1963 9 0.905784462 0.922851096 7
1964 LBJ 5.2 4 0.970212036 0.984982067 2
1965 3 0.978814616 0.990796696 1
1966 6 0.966284763 0.977612794 4
1967 5 0.967106177 0.977930996 3
1968 8 0.920402421 0.933475042 5
1969 Nixon 17.25 14 0.845067242 0.835257479 9
1970 22 0.704881829 0.650154781 16
1971 20 0.732382156 0.689598547 14
1972 13 0.857633796 0.863472246 8
1973 Ford 42.25 23 0.686493028 0.62434431 19
1974 55 0.33211444 0.179404386 44
1975 57 0.321579972 0.173544939 45
1976 34 0.509598854 0.36580158 28
1977 Carter 47.75 38 0.48539981 0.35316351 30
1978 36 0.490971433 0.348099757 32
1979 54 0.332565903 0.180633892 43
1980 63 0.233279209 0.097806068 49
1981 Regan 35.25 56 0.321743838 0.153677774 47
1982 49 0.381772329 0.208356036 41
1983 35 0.503995857 0.348178555 31
1984 32 0.54773218 0.406056681 24
1985 28 0.597794466 0.492928298 20
1986 21 0.710379159 0.639846296 17
1987 31 0.566435225 0.44269449 23
1988 30 0.580800208 0.462933805 22
1989 Bush 45.25 33 0.521165415 0.383677149 25
1990 46 0.412307398 0.270617242 38
1991 50 0.355030984 0.156074549 46
1992 52 0.345444204 0.182286727 42
1993 Clinton 34.125 51 0.347354183 0.211766125 40
1994 45 0.427773702 0.305690265 35
1995 43 0.437206907 0.304397475 36
1996 42 0.441622034 0.347118254 33
1997 29 0.590670423 0.475517612 21
1998 18 0.779123193 0.7652657 11
1999 19 0.739476814 0.756822966 12
2000 26 0.633036039 0.626496742 18
2001 Bush 44.5 27 0.623196359 0.679603264 15
2002 58 0.309619948 0.219628872 39
2003 39 0.461385592 0.317257402 34
2004 44 0.436895133 0.372577876 26
2005 48 0.392233115 0.279763426 37
2006 41 0.448807255 0.354638429 29
2007 37 0.485572252 0.370304654 27
2008 62 0.243657754 0.101303216 48
2009 Obama 72 68 0.185875697 0.04675897 50
2010 76 0.085299188 0.017963418 51

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Economic Data Results

2011-08-22T06:45:00.001-07:00

Below is the data that described and summarized in the last post.

Year Administration Administration Rank Year Rank Year Result
Lower is Better Higher is Better
1930 Hoover 67 70 0.130831887
1931 64 0.221734166
1932 FDR 61.84615385 71 0.122935228
1933 77 0.076671222
1934 78 0.049809404
1935 76 0.087318947
1936 72 0.121703976
1937 75 0.10023436
1938 56 0.311094304
1939 57 0.303852641
1940 54 0.334176301
1941 65 0.201564244
1942 69 0.150541944
1943 47 0.425730648
1944 7 0.916082866
1945 Truman 72.85714286 62 0.243494218
1946 81 0.009172218
1947 80 0.0120532
1948 79 0.028445666
1949 67 0.194375385
1950 68 0.178107058
1951 73 0.118795401
1952 13 0.858361589
1953 Eisenhower 17.75 1 0.994400033
1954 25 0.647240426
1955 2 0.98599563
1956 12 0.865117379
1957 26 0.640643105
1958 48 0.409641689
1959 10 0.889323577
1960 18 0.772019672
1961 JFK 13.33333333 20 0.73786751
1962 9 0.899980138
1963 11 0.880492743
1964 LBJ 5.2 4 0.959708924
1965 3 0.971483705
1966 6 0.957280896
1967 5 0.958452384
1968 8 0.902734896
1969 Nixon 19.25 16 0.815206674
1970 24 0.662566989
1971 23 0.695704785
1972 14 0.838745449
1973 Ford 47 27 0.638245657
1974 58 0.291196251
1975 61 0.269105837
1976 42 0.462022164
1977 Carter 53.5 45 0.450375938
1978 44 0.451813686
1979 59 0.288065432
1980 66 0.194419637
1981 Regan 37.25 60 0.274939679
1982 51 0.344763124
1983 39 0.489134255
1984 36 0.544792581
1985 30 0.609247419
1986 19 0.741096527
1987 32 0.593746561
1988 31 0.598178077
1989 Bush 47.75 37 0.524901208
1990 49 0.408674223
1991 52 0.341571053
1992 53 0.336825808
1993 Clinton 32.625 50 0.349906492
1994 46 0.445423691
1995 43 0.459736113
1996 41 0.466937537
1997 28 0.626830293
1998 15 0.83470193
1999 17 0.814179757
2000 21 0.728106057
2001 Bush 35.75 22 0.719220129
2002 40 0.474852286
2003 34 0.567047555
2004 35 0.554101245
2005 38 0.510451053
2006 33 0.582860235
2007 29 0.6187823
2008 55 0.314333976
2009 Obama 68.5 63 0.226752781
2010 74 0.103257823

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Making Sense of Economic Data

2011-08-22T06:41:00.001-07:00

For years pundits have argued which years have been the best or worst in our nation’s economic history. Pundits have argued which president led our economy through the best or worst times. Today, we continually hear the Obama administration claim we are going through the worst recession since the Great Depression. Is this true?

To answer these questions I have tried to compare our economic strengths and weaknesses annually from 1930 to the present. I have combined yearly unemployment, inflation, federal budget levels, federal debt levels, and our gross domestic product (GDP) into one metric or figure of merit. These are arguably the most critical yearly statistics to measure an economies wellness or sickness. The data is fitted into a normal distribution curve and ranked in order from best to worst economic years.

The presidential administrations from 1930 to the present ranked as follows (ratings are in brackets []. Each of 81 years is ranked 1 through 81. The ranking indicates the average yearly ranking for each administration.): first, Lyndon Johnson (LBJ) [5]; second, John Kennedy (JFK) [13]; third, Dwight Eisenhower [17]; fourth, Richard Nixon [19]; fifth, Bill Clinton [33]; sixth, George W. Bush [37]; seventh, Ronald Reagan [38]; eighth, Gerald Ford [47]; ninth, George H.W. Bush [48]; tenth, Jimmy Carter [54]; eleventh, Franklin Roosevelt (FDR) [62]; twelfth, Herbert Hoover [67]; thirteenth, Barack Obama [68]; fourteenth, Harry Truman [72]. This data can be misleading because it does not account for trends. For instance, Eisenhower could be credited with stabilizing the economy that benefited JFK and LBJ. Reagan was responsible for getting the Carter mess under control. And yes, although George W. Bush is ranked 6th, the economic status under his guidance got worse each year. Thus, he could have benefited from Clinton’s leadership and could be responsible for hurting Obama’s numbers.

The top 10 years according to this statistical average are as follows: 1. 1953 (.994); 2. 1955 (.986); 3. 1965 (.971); 4. 1964 (.960); 5. 1967 (.958); 6. 1966 (.957); 7. 1944 (.916); 8. 1968 (.903); 9. 1962 (.900); and 10. 1959 (.889). The ratings in parenthesis are a percentage as to where each year lies in the normal distribution curve. A rating of 0.50 is average, and any number higher than that is above average and any number lower than that is below average. The 10 worst years are as follows: 1. 1946 (.010); 2. 1945 (0.12); 3. 1948 (.028); 1934 (.050); 5. 1933 (.077); 6. 1935 (.087); 7. 1937 (.100); 8. 2010 (.103); 9. 1951 (.119); 10. 1936 (.122). In essence, the figure of merit for 1953 is at the 99.4 percentile while 1946 is at the 1 percentile in terms of economic stability.

Here is a list of each administrations best and worst years. Barack Obama: 2009 (.227), 2010 (.103); George W. Bush: 2001 (.719), 2008 (.314); Bill Clinton: 1998 (.837), 1993 (.350); George H.W. Bush: 1989 (.525), 1992 (.334); Ronald Reagan: 1986 (.741), 1981 (.274); Jimmy Carter: 1978 (.452), 1980 (.194); Gerald Ford: 1973 (.638), 1975 (.269); Richard Nixon: 1972 (.839), 1970 (.663); LBJ: 1965 (.971), 1968 (.903); JFK: 1962 (.900), 1961 (.738); Dwight Eisenhower: 1953 (.994), 1958 (.410); Harry Truman: 1952 (.858); 1946 (.010); FDR: 1944 (.916), 1934 (.050); Herbert Hoover: 1931 (.222), 1930 (.130).

I will post the entire results of this analysis in my next blog post.

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What Economic Indicators Have the Biggest Effect on the Economy? (Part II)

2011-08-18T11:02:00.001-07:00

When performing a linear regression model and solving for Debt, Trade Deficit, GDP, Inflation, Tax Revenue, Unemployment, Budget, and Consumer Spending we get the following equations:

Parameter Intercept Unemployment Inflation GDP Debt Budget Home Price Housing CPI Trade Deficit Tax Revenue Consumer Spending Auto Sales
Debt = -2376 -145.5 32.43 -1.792 0 3.052 -0.00231 0.9519 26.18 -0.004196 0.0003138 0.003041 -6.192
Trade Deficit = -1998 -32294 3619 -487 -114 1894 0.8807 254.1 -3220 0 0.04326 0.8848 -2816
GDP = 938.6 -66.82 -28.08 0 -0.1021 -0.1053 0.002069 0.2452 -7.214 -0.001058 4.89E-05 0.00169 -4.819
Inflation = 22.29 -0.5324 0 -0.00574 0.000391 -0.00753 3.15E-05 -0.01656 -0.1417 1.61E-06 -1.33E-06 1.02E-05 -0.03918
Tax Revenue = 1657362 -70587 -14010 105.7 40.01 -818.6 -3.349 -92.53 -9519 0.203 0 0.4324 -2671
Unemployment = 15.83 0 0.2722 -0.00693 -0.0009 -0.00198 -6.25E-06 0.001771 -0.1215 -7.34E-06 -3.42E-06 1.42E-05 -5.22E-02
Budget = 1382 -15.92 -31.05 -0.08868 0.1518 0 0.0007186 -0.1167 -11 0.0003468 0.0003193 0.000348 -2.086
Consumer Spending = -691475 44461 16049 544.7 57.85 133.1 -0.7387 -143.9 5940 0.6197 0.06453 0 3156

For example, for the national Debt the equation is equal to -2376 – 145.5 x Unemployment + 32.43 x Inflation – 1.792 x GDP + 3.052 x Budget – 0.00231 x Home Price + .9519 x Housing Startups + 26.18 x CPI – 0.004196 x Trade Deficit + 0.0003138 x Tax Revenue + 0.003041 x Consumer Spending – 6.192 x Auto Sales.

We can learn a lot about our economy by evaluating each equation. For instance, the debt equation shows that the debt will decrease as unemployment increases; the debt will go up as inflation increases; the debt will decrease as GDP goes up; the debt will increase as the national budget increases; the debt will go down as home prices increase; the debt will go up as housing starts increase; the debt will go up as Consumer Price Index (CPI) goes up; the debt will go down as the trade deficit increases, the debt will go up with tax revenue increases; the debt will go up with consumer spending; and the debt will go down as auto sales increase.

It certainly is not surprising that our national debt is lowered if GDP or other economic indicators such as auto sales or home prices go up. And it is certainly not surprising that our national debt goes up if the federal budget increases, or if there is higher inflation, or if CPI prices increase. There are however, some surprising results – in particular, even as tax revenues and consumer spending increase the debt continues to go up – that is because as the government receives more in tax revenues, they spend more money. This result is apparent by reviewing the results of the other 7 equations. But there are some mind boggling results. For instance, if unemployment and our trade deficit increase it would yield a lower national debt. This actually states that the federal government tends to be more frugal during recessions – this is certainly not the case with our current president.

But there are some other more logical explanations for the strange results in the national debt formula. First, the intercept value starts negative (so there has already been a negative compensation in the formula), so there has to be more upward movement in the formula. Secondly, unemployment and the trade deficit are in the bottom five parameters that affect our economy. The impact of each economic indicator can be weighted in each of the above 8 equations. The 4 most significant parameters that affect the economy are GDP, Consumer Spending, Debt, and the national Budget. The parameters that have the smallest impact on the economy are inflation, tax revenues, and the trade deficit.

Thus, a thriving economy is one where GDP and consumer spending are maximized whereas tax revenues are minimized while at the same time balancing the budget. In other words, we need a society that emphasizes capitalism over government interference. The bottom line is that higher tax revenues do little to fix the budget deficit because the federal government irresponsibly spends any additional revenues it receives.

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What Economic Indicators Have the Biggest Impact on the Economy (Part I)?

2011-08-15T07:47:00.001-07:00

I built an economic model using the following leading indicators from 1982 to 2010 (some parameters data only goes back to 1982): Unemployment, Inflation, Debt, Trade Deficit, Consumer Price Index, the Gross Domestic Product (GDP), Consumer Spending, Auto Sales, Tax Revenue, Housing Prices, and Housing Start Ups. The data is readily available from the Bureau of Economic Analysis (BEA). The model is below (the parameters are in percent or thousands or millions of dollars).

Year Unemployment Inflation GDP Debt Budget Home Price Housing Starts CPI Trade Deficit Tax Revenue Consumer Spending Auto Sales
1982 9.7 6.16 3253 1142 746 83900 1,062.2 42.29 24156 617766 2075500 79.6
1983 9.6 3.22 3534 1377 808 89800 1,703.0 43.29 57767 600562 2288600 100.8
1984 7.5 4.3 3931 1572 852 97600 1,749.5 45.25 109072 666438 2501100 124.4
1985 7.2 3.55 4218 1823 946 100800 1,741.8 41.45 121880 734037 2717600 146.6
1986 7 1.91 4460 2125 990 111900 1,805.4 45.62 138563 769155 2896700 164
1987 6.2 3.66 4736 2350 1004 127200 1,620.5 45.61 151684 854288 3097000 164.2
1988 5.5 4.08 5100 2602 1064 138300 1,488.1 45.52 114566 909238 3350100 175.9
1989 5.3 4.83 5482 2857 1144 148800 1,376.1 46.93 93141 991105 3594500 180.8
1990 5.6 5.39 5801 3233 1253 149800 1,192.7 48.21 80864 1031922 3835500 176.9
1991 6.8 4.25 5992 3665 1324 147200 1,013.9 49.6 31135 1054996 3980100 157.9
1992 7.5 3.03 6342 4064 1381 144100 1,199.7 51.17 39212 1091223 4236900 176.1
1993 6.9 2.96 6667 4411 1409 147700 1,287.6 53.46 70311 1154341 4483600 193.9
1994 6.1 2.61 7085 4692 1462 154500 1,457.0 53.99 98493 1258579 4750800 216.1
1995 5.6 2.81 7414 4974 1516 158700 1,354.1 54.66 96384 1351801 4987300 220.3
1996 5.4 2.93 7839 5225 1561 166400 1,476.8 55.29 104065 1453055 5273600 235.8
1997 4.9 2.34 8332 5413 1601 176200 1,474.0 55.45 108273 1579240 5570600 254
1998 4.5 1.55 8794 5526 1653 181900 1,616.9 51.56 166140 1721733 5918500 280.7
1999 4.2 2.19 9354 5656 1702 195600 1,640.9 51.23 264239 1822459 6342800 309.7
2000 4 3.38 9951 5674 1789 207000 1,568.7 52.47 378780 2025198 6830400 321.4
2001 4.7 2.83 10286 5807 1863 213200 1,602.7 55 364393 1991142 7148800 342
2002 5.8 1.59 10642 6228 2011 228700 1,704.9 54.45 420524 1853149 7439200 359.7
2003 6 2.27 11142 6783 2160 246300 1,847.7 56.18 494183 1782321 7804000 358.2
2004 5.5 2.68 11868 7379 2293 274500 1,955.8 57.71 609345 1880126 8285100 359.5
2005 5.1 3.39 12638 7933 2472 297000 2,068.3 61.05 714176 2153625 8819000 361.6
2006 4.6 3.24 13398 8507 2655 305900 1,800.9 66.63 759240 2406876 9322700 346.6
2007 4.6 2.85 14077 9008 2729 313600 1,355.0 69.72 702099 2568001 9806300 349.9
2008 5.8 3.85 14441 10023 2982 292600 905.5 75.36 698802 2523991 10104500 291
2009 9.3 -0.34 14256 11910 3518 270900 554.0 75.41 374908 2104989 10001300 269.4
2010 9.6 1 14500 13300 3721 272400 586.9 75.89 497824 2162724 10500000 303

In part 2 of this blog I will evaluate the impact of the parameters in the above model by finding a linear regression solution.

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Unemployment Model

2011-08-13T10:21:00.001-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the unemployment economic parameter:

n 64

R2 0.79
Adjusted R2 0.74
SE 0.82

Term Coefficient 95% CI SE t statistic DF p
Intercept 5.545 1.916 to 9.175 1.8070 3.07 50 0.0035
Population 0.002931 -0.017709 to 0.023570 0.0102759 0.29 50 0.7767
Inflation -0.1406 -0.2205 to -0.0606 0.03980 -3.53 50 0.0009
GDP -0.00264 -0.00742 to 0.00214 0.002379 -1.11 50 0.2725
Debt -0.004598 -0.006006 to -0.003191 0.0007007 -6.56 50 <0.0001
Tax Receipts -0.001045 -0.009708 to 0.007618 0.0043131 -0.24 50 0.8095
Gov Spending 0.00789 -0.00145 to 0.01723 0.004649 1.70 50 0.0959
Budget -0.002198 -0.013211 to 0.008816 0.0054834 -0.40 50 0.6903
Trade Deficit 0.007352 0.000805 to 0.013899 0.0032594 2.26 50 0.0285
Consumer Spending 0.003819 -0.007226 to 0.014864 0.0054991 0.69 50 0.4906
State Deficit 0.01752 -0.00862 to 0.04366 0.013014 1.35 50 0.1843
State Social Payment -0.01106 -0.07132 to 0.04920 0.030001 -0.37 50 0.7139
Gov Social Benefits 0.03103 0.01167 to 0.05038 0.009637 3.22 50 0.0023
Personal Income -0.002082 -0.009859 to 0.005695 0.0038719 -0.54 50 0.5932

The economic parameters used to model unemployment over the past 64 (n) years are: the U.S. population, the inflation rate, personal income, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, federal government spending, the federal government budget levels, the federal trade deficit, consumer spending, state government budget levels, state government spending on social benefits, and federal government social benefit payments. The intercept value in the above table is not a parameter – it is the value of unemployment (in percent) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Unemployment in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Unemployment in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Unemployment in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Unemployment in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on unemployment?

In times of high unemployment the federal government debt is increased as it tries to spend its way out of a recession. Once inflation and GDP increase then unemployment levels decrease. But it is important to note that increased federal social benefit payments does not lead to lower unemployment.

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Personal Income Model

2011-08-08T06:41:00.001-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the personal income economic parameter:

n 64

R2 1.00
Adjusted R2 1.00
SE 29.91

Term Coefficient 95% CI SE t statistic DF p
Intercept 65.45 -77.45 to 208.35 71.144 0.92 50 0.3620
Population -0.3092 -1.0563 to 0.4380 0.37199 -0.83 50 0.4098
Unemployment -2.761 -13.077 to 7.554 5.1358 -0.54 50 0.5932
Inflation -1.952 -5.159 to 1.255 1.5966 -1.22 50 0.2272
GDP 0.142 -0.030 to 0.313 0.0854 1.66 50 0.1026
Debt -0.05684 -0.12488 to 0.01121 0.033877 -1.68 50 0.0996
Tax Receipts 0.5459 0.2709 to 0.8209 0.13691 3.99 50 0.0002
Gov Spending -0.2901 -0.6300 to 0.0498 0.16921 -1.71 50 0.0927
Budget -0.04835 -0.44988 to 0.35318 0.199910 -0.24 50 0.8099
Trade Deficit 0.4869 0.2783 to 0.6955 0.10385 4.69 50 <0.0001
Consumer Spending 0.9265 0.6197 to 1.2333 0.15274 6.07 50 <0.0001
State Social Payment -5.385 -6.963 to -3.807 0.7856 -6.85 50 <0.0001
Gov Social Benefits 1.845 1.274 to 2.415 0.2840 6.49 50 <0.0001
State Deficit -1.496 -2.367 to -0.625 0.4336 -3.45 50 0.0011

The economic parameters used to model personal income over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, federal government spending, the federal government budget levels, the federal trade deficit, consumer spending, state government budget levels, state government spending on social benefits, and federal government social benefit payments. The intercept value in the above table is not a parameter – it is the value of personal income (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Personal Income in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Personal Income in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Personal Income in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Personal Income in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on personal income?

It should come as no surprise when economic times are good and GDP, federal tax receipts, the trade deficit, and consumer spending are all increasing then so is personal income. Generally, when state and federal government spending and debt levels increase then personal income is declining. It is interesting to note that personal income levels have been decreasing with population increases.

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A Federal Government Social Benefits Model

2011-07-26T08:36:00.000-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the federal government social benefit economic parameter:

n 64

R2 1.00
Adjusted R2 1.00
SE 10.97

Term Coefficient 95% CI SE t statistic DF p
Intercept -55.01 -105.49 to -4.53 25.133 -2.19 50 0.0333
Population 0.09296 -0.18166 to 0.36757 0.136724 0.68 50 0.4997
Unemployment 5.535 2.082 to 8.987 1.7190 3.22 50 0.0023
Inflation 1.905 0.842 to 2.969 0.5296 3.60 50 0.0007
GDP -0.0464 -0.1096 to 0.0168 0.03149 -1.47 50 0.1469
Debt 0.04623 0.02420 to 0.06826 0.010967 4.22 50 0.0001
Tax Receipts -0.1289 -0.2387 to -0.0191 0.05468 -2.36 50 0.0224
Gov Spending 0.03361 -0.09428 to 0.16151 0.063674 0.53 50 0.5999
Budget 0.1459 0.0045 to 0.2873 0.07039 2.07 50 0.0434
Trade Deficit -0.1554 -0.2358 to -0.0749 0.04006 -3.88 50 0.0003
Consumer Spending -0.2064 -0.3426 to -0.0703 0.06778 -3.05 50 0.0037
State Deficit 0.2576 -0.0902 to 0.6053 0.17314 1.49 50 0.1431
State Social Payment 2.005 1.435 to 2.575 0.2839 7.06 50 <0.0001
Personal Income 0.2481 0.1713 to 0.3248 0.03820 6.49 50 <0.0001

The economic parameters used to model federal government social benefit payments over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, federal government spending, the federal government budget levels, the federal trade deficit, consumer spending, state government budget levels, state government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of federal government social benefit payments (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Federal Social Benefits in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Federal Social Benefits in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Federal Social Benefits in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Federal Social Benefits in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on federal government social benefit expenditures?

It should come as no surprise that as unemployment, inflation, federal debt, the federal budget, state deficits, and state social benefit payments all increase then federal social benefit payments also increase. As economic times improve and GDP, federal tax receipts, the trade deficit, and consumer spending increase then federal social benefit payments decrease.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

State Social Benefits Model

2011-07-17T07:33:00.000-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the individual state social benefit economic parameter:

n 64

R2 1.00
Adjusted R2 1.00
SE 3.87

Term Coefficient 95% CI SE t statistic DF p
Intercept -0.05944 -18.68551 to 18.56664 9.273352 -0.01 50 0.9949
Population -0.006422 -0.103646 to 0.090803 0.0484051 -0.13 50 0.8950
Unemployment -0.2451 -1.5805 to 1.0903 0.66485 -0.37 50 0.7139
Inflation -0.1318 -0.5508 to 0.2872 0.20860 -0.63 50 0.5303
GDP 0.006232 -0.016470 to 0.028933 0.0113023 0.55 50 0.5838
Debt -0.0006797 -0.0097169 to 0.0083575 0.00449934 -0.15 50 0.8805
Tax Receipts 0.005785 -0.034988 to 0.046557 0.0202994 0.28 50 0.7768
Gov Spending -0.003856 -0.049047 to 0.041334 0.0224990 -0.17 50 0.8646
Budget -0.08189 -0.12832 to -0.03546 0.023116 -3.54 50 0.0009
Trade Deficit 0.05975 0.03221 to 0.08729 0.013711 4.36 50 <0.0001
Consumer Spending 0.129 0.092 to 0.166 0.0185 6.96 50 <0.0001
State Deficit -0.06561 -0.18947 to 0.05826 0.061669 -1.06 50 0.2925
Gov Social Benefits 0.2491 0.1782 to 0.3199 0.03527 7.06 50 <0.0001
Personal Income -0.08997 -0.11633 to -0.06360 0.013125 -6.85 50 <0.0001

The economic parameters used to model state government social benefit payments over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, federal government spending, the federal government budget levels, the federal trade deficit, consumer spending, state government budget levels, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of state government social benefit payments (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (State Social Benefits in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (State Social Benefits in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (State Social Benefits in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (State Social Benefits in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on state government social benefit expenditures?

As the federal government increases social benefit payments so do the states. Also, as consumers increase spending and the trade deficit increases so do state social benefit payments. As personal incomes and state deficits increase then state social benefit payments decrease. It is strange how state government budget deficits and social benefit payments are inversely related in this model.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

State Budget Model

2011-07-11T06:35:00.001-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the individual state budget economic parameter:

n 64

R2 0.96
Adjusted R2 0.95
SE 8.77

Term Coefficient 95% CI SE t statistic DF p
Intercept -23.78 -65.48 to 17.91 20.759 -1.15 50 0.2573
Population 0.08521 -0.13397 to 0.30439 0.109125 0.78 50 0.4386
Unemployment 1.997 -0.982 to 4.975 1.4831 1.35 50 0.1843
Inflation 0.1056 -0.8478 to 1.0591 0.47469 0.22 50 0.8249
GDP 0.0491 -0.0006 to 0.0988 0.02475 1.98 50 0.0528
Debt 0.01429 -0.00580 to 0.03439 0.010003 1.43 50 0.1593
Tax Receipts 0.2714 0.2202 to 0.3226 0.02549 10.65 50 <0.0001
Gov Spending -0.2278 -0.3073 to -0.1483 0.03958 -5.76 50 <0.0001
Budget 0.2375 0.1410 to 0.3341 0.04805 4.94 50 <0.0001
Trade Deficit -0.05445 -0.12616 to 0.01725 0.035701 -1.53 50 0.1335
Consumer Spending -0.03384 -0.15193 to 0.08425 0.058793 -0.58 50 0.5675
Gov Social Benefits 0.1646 -0.0576 to 0.3867 0.11062 1.49 50 0.1431
State Social Payment -0.3374 -0.9744 to 0.2996 0.31713 -1.06 50 0.2925
Personal Income -0.1285 -0.2034 to -0.0537 0.03726 -3.45 50 0.0011

The economic parameters used to model state government deficit levels over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, federal government spending, the federal government budget levels, the federal trade deficit, consumer spending, state government spending on social benefits, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of state government budget levels (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (State Budgets in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (State Budgets in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (State Budgets in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (State Budgets in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on state budgets?

It should come as no surprise as the federal government raises more tax revenues, spends more on entitlements, and increase their budget and deficit levels – so do the individual states. On the other hand, increased levels in personal income, consumer spending, and federal government spending can lead to lower budget deficits (states can collect more in sales taxes).

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

Federal Trade Deficit Model (Updated)

2011-07-05T11:45:00.000-07:00

Just for CW, I recalculated the Federal Trade Deficit Model with the inclusion of two more parameters: the Dollar Value and Manufacturing Jobs. Manufacturing jobs is really already partly included in the unemployment figures parameter, and like the unemployment variable, the decline of manufacturing jobs over the past 60 years does not have much effect on the federal trade deficit (low t statistic). On the other hand, the value of the dollar has a very strong correlation with the trade deficit, as one would suspect (thanks CW for pointing out this omission). As the value of the dollar increases in value the trade deficit will decrease. By adding these two parameters into the model, it does increase both the accuracy and predictability of the model. Also, by adding these parameters to other economic models I have created such as the GDP Model, Inflation Model, Unemployment Model, and so on, it also improves those models (although it only improves them, on average, by about 1 to 2 percent). Interestingly, in this model, Personal Income and Dollar Value are complete opposites (equal magnitude but opposite polarity).

R2 0.99
Adjusted R2 0.98
SE 28.72

Term Coefficient 95% CI SE t statistic DF p
Intercept 859.9 187.2 to 1532.6 334.56 2.57 48 0.0133
Population -2.474 -4.242 to -0.706 0.8791 -2.81 48 0.0071
Unemployment 6.821 -9.481 to 23.123 8.1079 0.84 48 0.4044
Inflation 0.05206 -3.24278 to 3.34690 1.638705 0.03 48 0.9748
GDP 0.1196 -0.0472 to 0.2864 0.08297 1.44 48 0.1560
Debt 0.2018 0.1419 to 0.2617 0.02980 6.77 48 <0.0001
Tax Receipts 0.2149 -0.0898 to 0.5197 0.15156 1.42 48 0.1626
Gov Spending -0.4649 -0.8331 to -0.0967 0.18314 -2.54 48 0.0144
Budget 0.3966 0.0233 to 0.7699 0.18566 2.14 48 0.0378
Consumer Spending -0.9805 -1.2533 to -0.7078 0.13567 -7.23 48 <0.0001
State Deficit -1.594 -2.576 to -0.612 0.4882 -3.27 48 0.0020
State Social Payment 3.317 1.430 to 5.205 0.9389 3.53 48 0.0009
Gov Social Benefits -0.9611 -1.6984 to -0.2239 0.36670 -2.62 48 0.0117
Personal Income 0.4515 0.2116 to 0.6914 0.11931 3.78 48 0.0004
Dollar Value -412.1 -631.6 to -192.6 109.15 -3.78 48 0.0004
Manufacturing Jobs 0.002455 -0.020017 to 0.024927 0.0111767 0.22 48 0.8271

Consumer Spending Model

2011-07-04T11:29:00.001-07:00

Below are the results of a running linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the consumer spending economic variable:

n 64

R2 1.00
Adjusted R2 1.00
SE 21.02

Term Coefficient 95% CI SE t statistic DF p
Intercept -39.02 -139.68 to 61.64 50.115 -0.78 50 0.4398
Population 0.2086 -0.3167 to 0.7340 0.26156 0.80 50 0.4288
Unemployment 2.502 -4.734 to 9.737 3.6022 0.69 50 0.4906
Inflation 0.4412 -1.8424 to 2.7248 1.13692 0.39 50 0.6996
GDP 0.1883 0.0766 to 0.2999 0.05559 3.39 50 0.0014
Debt 0.05213 0.00527 to 0.09899 0.023331 2.23 50 0.0300
Tax Receipts 0.005178 -0.216671 to 0.227026 0.1104516 0.05 50 0.9628
Gov Spending -0.08237 -0.32702 to 0.16228 0.121803 -0.68 50 0.5020
Budget 0.2829 0.0122 to 0.5536 0.13476 2.10 50 0.0408
Trade Deficit -0.5131 -0.6115 to -0.4147 0.04901 -10.47 50 <0.0001
State Deficit -0.1945 -0.8733 to 0.4843 0.33794 -0.58 50 0.5675
State Social Payment 3.814 2.714 to 4.915 0.5479 6.96 50 <0.0001
Gov Social Benefits -0.7581 -1.2581 to -0.2582 0.24890 -3.05 50 0.0037
Personal Income 0.4576 0.3061 to 0.6091 0.07543 6.07 50 <0.0001

The economic parameters used to model consumer spending over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, federal government spending, the federal government budget levels, the federal trade deficit, state government deficits, state government spending on social benefits, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of consumer spending (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Consumer Spending in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Consumer Spending in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Consumer Spending in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Consumer Spending in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on consumer spending?

Increased personal income, GDP, the federal deficit, and federal budget levels lead to more consumer spending. When times are good not only do individuals spend more money the federal government also spends more. Increased federal government payments in social benefits and increased trade deficit levels lead to lower consumer spending (in other words bad economic times).

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

Federal Deficit Model

2011-06-28T13:06:00.001-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the U.S. trade deficit economic variable:

n 64

R2 0.98
Adjusted R2 0.98
SE 33.95

Term Coefficient 95% CI SE t statistic DF p
Intercept -227.5 -377.7 to -77.2 74.80 -3.04 50 0.0037
Population 0.9905 0.1843 to 1.7967 0.40138 2.47 50 0.0171
Unemployment 12.56 1.38 to 23.75 5.569 2.26 50 0.0285
Inflation 2.745 -0.866 to 6.355 1.7976 1.53 50 0.1331
GDP 0.1939 0.0017 to 0.3861 0.09570 2.03 50 0.0481
Debt 0.1539 0.0876 to 0.2201 0.03299 4.66 50 <0.0001
Tax Receipts 0.1222 -0.2344 to 0.4788 0.17755 0.69 50 0.4945
Gov Spending -0.1355 -0.5305 to 0.2596 0.19669 -0.69 50 0.4941
Budget 0.4444 0.0062 to 0.8826 0.21816 2.04 50 0.0469
Consumer Spending -1.338 -1.595 to -1.082 0.1278 -10.47 50 <0.0001
State Deficit -0.8165 -1.8917 to 0.2587 0.53530 -1.53 50 0.1335
State Social Payment 4.607 2.484 to 6.730 1.0572 4.36 50 <0.0001
Gov Social Benefits -1.488 -2.259 to -0.717 0.3838 -3.88 50 0.0003
Personal Income 0.6272 0.3585 to 0.8959 0.13378 4.69 50 <0.0001

The economic parameters used to model the U.S. trade deficit over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, federal government spending, the federal government budget levels, consumer spending, state government deficits, state government spending on social benefits, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of the U.S. trade deficit (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Trade Deficit in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Trade Deficit in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Trade Deficit in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Trade Deficit in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on the U.S. trade deficit?

Increased U.S. population, unemployment, inflation, GDP, federal debt, federal budget levels, state social payments, and personal income have negative effects on the trade deficit. Consumer spending and government social benefit payments have the biggest impact to lower our trade deficit. As people and the government spend less money on exports – the trade deficit will decline.

My Book: Is America Dying? (Amazon.com, Barnes and Noble)

Federal Government Budget Model

2011-06-23T13:27:00.001-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the U.S. federal government budget economic variable:

n 64

R2 1.00
Adjusted R2 1.00
SE 21.1

Term Coefficient 95% CI SE t statistic DF p
Intercept -4.498 -106.370 to 97.375 50.7193 -0.09 50 0.9297
Population 0.06869 -0.46284 to 0.60022 0.264634 0.26 50 0.7963
Unemployment -1.457 -8.760 to 5.845 3.6357 -0.40 50 0.6903
Inflation -0.1558 -2.4562 to 2.1447 1.14532 -0.14 50 0.8924
GDP -0.02328 -0.14766 to 0.10109 0.061921 -0.38 50 0.7085
Debt -0.001651 -0.051092 to 0.047789 0.0246149 -0.07 50 0.9468
Tax Receipts -0.4659 -0.6457 to -0.2862 0.08948 -5.21 50 <0.0001
Gov Spending 0.6586 0.4970 to 0.8203 0.08049 8.18 50 <0.0001
Trade Deficit 0.1724 0.0024 to 0.3425 0.08465 2.04 50 0.0469
Consumer Spending 0.2863 0.0124 to 0.5603 0.13639 2.10 50 0.0408
State Deficit 1.382 0.821 to 1.944 0.2796 4.94 50 <0.0001
State Social Payment -2.45 -3.84 to -1.06 0.692 -3.54 50 0.0009
Gov Social Benefits 0.5422 0.0167 to 1.0677 0.26165 2.07 50 0.0434
Personal Income -0.02417 -0.22488 to 0.17654 0.099928 -0.24 50 0.8099

The economic parameters used to model Government Budget levels over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, federal government spending, the trade deficit, consumer spending, state government deficits, state government spending on social benefits, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of the Government Budget (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Government Budget in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Government Budget in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Government Budget in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Government Budget in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on the U.S. federal government budget?

Not surprisingly, government spending, state deficits, government social benefits, the trade deficit, and consumer spending have the biggest impact on the budget size. The only thing that will significantly lower the federal budget size is if states increase their share of social payments. If tax receipts go up, the government can also control the federal budget from going further into debt.

GDP Model

2011-06-23T13:03:00.003-07:00

Here is another set of models I ran on economic indicators which support my earlier claims from previous posts.

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the Gross Domestic Product (GDP) variable:

n 64

R2 1.00
Adjusted R2 1.00
SE 48.2

Term Coefficient 95% CI SE t statistic DF p
Intercept 27.19 -205.03 to 259.42 115.619 0.24 50 0.8150
Population 0.3053 -0.9047 to 1.5152 0.60240 0.51 50 0.6146
Unemployment -9.103 -25.583 to 7.377 8.2047 -1.11 50 0.2725
Inflation -0.04267 -5.29017 to 5.20484 2.612572 -0.02 50 0.9870
Debt -0.1244 -0.2315 to -0.0173 0.05331 -2.33 50 0.0237
Tax Receipts -0.1041 -0.6123 to 0.4041 0.25301 -0.41 50 0.6825
Gov Spending 0.7386 0.2151 to 1.2620 0.26060 2.83 50 0.0066
Budget -0.1211 -0.7680 to 0.5258 0.32208 -0.38 50 0.7085
Trade Deficit 0.3913 0.0034 to 0.7793 0.19314 2.03 50 0.0481
Consumer Spending 0.9912 0.4034 to 1.5790 0.29265 3.39 50 0.0014
State Deficit 1.486 -0.019 to 2.990 0.7491 1.98 50 0.0528
State Social Payment 0.9698 -2.5630 to 4.5025 1.75886 0.55 50 0.5838
Gov Social Benefits -0.897 -2.120 to 0.326 0.6088 -1.47 50 0.1469
Personal Income 0.3691 -0.0767 to 0.8150 0.22198 1.66 50 0.1026

The economic parameters used to model GDP over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the federal government debt, federal government tax receipts, federal government spending, the federal government budget size, the trade deficit, consumer spending, state government deficits, state government spending on social benefits, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of GDP (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (GDP in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (GDP in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (GDP in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (GDP in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on GDP?

Consumer spending has the biggest positive impact on GDP and economic growth. Government spending can increase GDP as well, but increased tax receipts have a negative effect. Unemployment, deficit spending (Debt), government expenditures for social benefits, and increased federal budgets all have a negative effect on GDP. An increased trade deficit also increases GDP – it means consumers and both the federal and state governments are spending more money to import products including oil (our biggest import). These results are not surprising and support conservative fiscal claims for decades.

Federal Debt Model

2011-06-23T13:02:00.001-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the U.S. federal government Debt economic parameter:

n 64

R2 1.00
Adjusted R2 1.00
SE 121.5

Term Coefficient 95% CI SE t statistic DF p
Intercept 1376 941 to 1812 216.9 6.35 50 <0.0001
Population -3.83 -6.69 to -0.97 1.422 -2.69 50 0.0096
Unemployment -100.6 -131.4 to -69.8 15.33 -6.56 50 <0.0001
Inflation -26.64 -37.48 to -15.81 5.396 -4.94 50 <0.0001
GDP -0.7892 -1.4687 to -0.1097 0.33831 -2.33 50 0.0237
Tax Receipts 0.6127 -0.6578 to 1.8831 0.63251 0.97 50 0.3374
Gov Spending 1.05 -0.34 to 2.44 0.691 1.52 50 0.1353
Budget -0.0545 -1.6863 to 1.5773 0.81244 -0.07 50 0.9468
Trade Deficit 1.971 1.122 to 2.819 0.4225 4.66 50 <0.0001
Consumer Spending 1.742 0.176 to 3.307 0.7794 2.23 50 0.0300
State Deficit 2.745 -1.113 to 6.603 1.9209 1.43 50 0.1593
State Social Payment -0.6712 -9.5954 to 8.2530 4.44307 -0.15 50 0.8805
Gov Social Benefits 5.672 2.969 to 8.374 1.3455 4.22 50 0.0001
Personal Income -0.9377 -2.0603 to 0.1849 0.55891 -1.68 50 0.0996

The economic parameters used to model Debt over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government tax receipts, federal government spending, the federal government budget size, the trade deficit, consumer spending, state government deficits, state government spending on social benefits, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of the Debt (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Debt in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Debt in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Debt in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Debt in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on the U.S. federal government Debt?

The trade deficit has the biggest negative impact on our debt (importing oil) as does both federal and state government spending (in particular spending on government social benefits). Increased inflation and GDP can drive the federal government debt lower. The odd statistic is that increased unemployment tends to lower debt and increased tax receipts raise the debt. Actually, this is not that odd since the federal government tends to spend money than it receives regardless as to whether or not they raise more tax revenue. Thus, the federal debt continues to go up even when consumer spending is high

Federal Tax Receipts Model

2011-06-23T12:59:00.000-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the U.S. federal government Tax Receipts economic variable:

n 64

R2 1.00
Adjusted R2 1.00
SE 26.91

Term Coefficient 95% CI SE t statistic DF p
Intercept -16.14 -145.72 to 113.43 64.513 -0.25 50 0.8034
Population -0.03525 -0.71208 to 0.64159 0.336976 -0.10 50 0.9171
Unemployment -1.122 -10.425 to 8.181 4.6316 -0.24 50 0.8095
Inflation 1.511 -1.386 to 4.407 1.4421 1.05 50 0.2998
GDP -0.03242 -0.19065 to 0.12582 0.078782 -0.41 50 0.6825
Debt 0.03006 -0.03228 to 0.09240 0.031038 0.97 50 0.3374
Gov Spending 0.5752 0.3063 to 0.8441 0.13389 4.30 50 <0.0001
Budget -0.7547 -1.0457 to -0.4636 0.14492 -5.21 50 <0.0001
Trade Deficit 0.07679 -0.14733 to 0.30092 0.111584 0.69 50 0.4945
Consumer Spending 0.008488 -0.355196 to 0.372171 0.1810668 0.05 50 0.9628
State Social Payment 0.2803 -1.6954 to 2.2560 0.98365 0.28 50 0.7768
Gov Social Benefits -0.776 -1.437 to -0.115 0.3292 -2.36 50 0.0224
Personal Income 0.442 0.219 to 0.665 0.1108 3.99 50 0.0002
State Deficit 2.557 2.075 to 3.039 0.2402 10.65 50 <0.0001

The economic parameters used to model Tax Receipts over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government spending, the federal government budget size, the trade deficit, consumer spending, state government deficits, state government spending on social benefits, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of Tax Receipts (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Tax Receipts in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Tax Receipts in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Tax Receipts in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Tax Receipts in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on U.S. federal government Tax Receipts?

As personal income, federal government spending, and state government debt increase so does the amount of tax receipts received by the federal government. This, once again, proves the federal government will spend more if it receives more revenue. The federal deficit continues to increase even as the government receives more tax revenue. Interestingly, the only thing that will help reduce federal government tax revenues is to reduce the budget, and in particular, government social benefit spending. Our current government is following the opposite strategy.

Federal Government Spending Model

2011-06-23T12:58:00.000-07:00

Below are the results of running a linear regression model on various economic data (obtained from the Bureau of Economic Analysis [BEA] government site) from 1947 to the present solving for the U.S. federal government spending economic variable:

n 64

R2 1.00
Adjusted R2 1.00
SE 24.29

Term Coefficient 95% CI SE t statistic DF p
Intercept -61.46 -177.20 to 54.27 57.620 -1.07 50 0.2912
Population 0.05713 -0.55371 to 0.66796 0.304115 0.19 50 0.8518
Unemployment 6.904 -1.266 to 15.075 4.0679 1.70 50 0.0959
Inflation 0.537 -2.102 to 3.176 1.3138 0.41 50 0.6845
GDP 0.1874 0.0546 to 0.3202 0.06612 2.83 50 0.0066
Debt 0.04197 -0.01357 to 0.09751 0.027650 1.52 50 0.1353
Tax Receipts 0.4687 0.2496 to 0.6878 0.10910 4.30 50 <0.0001
Trade Deficit -0.06938 -0.27169 to 0.13294 0.100727 -0.69 50 0.4941
Consumer Spending -0.11 -0.44 to 0.22 0.163 -0.68 50 0.5020
State Deficit -1.749 -2.360 to -1.139 0.3039 -5.76 50 <0.0001
State Social Payment -0.1523 -1.9367 to 1.6321 0.88841 -0.17 50 0.8646
Gov Social Benefits 0.1649 -0.4625 to 0.7923 0.31236 0.53 50 0.5999
Personal Income -0.1914 -0.4156 to 0.0328 0.11163 -1.71 50 0.0927
Budget 0.8692 0.6559 to 1.0826 0.10623 8.18 50 <0.0001

The economic parameters used to model Government Spending over the past 64 (n) years are: the U.S. population, the unemployment rate, the inflation rate, the U.S. Gross Domestic Product (GDP), federal government debt, federal government tax receipts, the federal government budget size, the trade deficit, consumer spending, state government deficits, state government spending on social benefits, federal government spending on social benefits, and personal income. The intercept value in the above table is not a parameter – it is the value of the Government Spending (in billions of dollars) if all other parameters equal zero. These economic parameters are denoted in the above table.

The R² statistic illustrates how closely the linear regression model resembles a straight line (the ideal condition). If R² equals one then the model is 100% linear and the parameters correlate 100%. On the other hand, if R² is equal to zero then there is no correlation and the data in the linear regression model is completely random. T statistics reveal which of the economic parameters has the best correlation to the parameter being tested (Government Spending in this case). The higher the absolute value of the t statistic, the better the correlation the corresponding economic parameter has to the tested variable (Government Spending in this case). If a coefficient value of an economic parameter is positive then it trends in the same direction of the tested variable (Government Spending in this case). If a coefficient value is negative then the corresponding variable trends in the opposite direction of the tested variable (Government Spending in this case). It is time to do some math to prove higher taxes and government spending cripple economies. What economic parameters have the biggest effect on U.S. federal government spending?

Tax Receipts! The more money the federal government receives the more money it squanders. Other factors such as increased GDP, federal debt, the federal budget, and unemployment play a big role in increased government spending. The only thing that significantly lowers federal government spending is if state governments’ are running higher deficits (spending more) and if personal income drops.

What Caused the Financial/Housing Collapse?

2011-06-23T11:01:00.000-07:00

Let’s do the math. Over 97% of U.S. mortgage debt has been handed out by commercial banks, savings institutions, credit unions, government sponsored programs, agency and government sponsored programs, asset backed programs, and financial institutions. We can model mortgages handed out through these programs/institutions as well as those mortgage backed securities (MBS - both private and government that were handed out by the before mentioned mortgage institutions and programs), that are believed to have caused the financial/housing collapse, against the results of the Dow Jones, S&P500, NYSE, NASDAQ, foreclosure rates, home ownership rate, home vacancy rate, and the mortgage payment delinquency rate. The data used in the model came from the Census Bureau, Market sites, and SIFMA statistical research company. Here are the results (I will draw conclusions at the end of each section. I will cover the market results in this post and the economic indicator results in Part II): Dow Jones R2 0.87 Adjusted R2 0.75 SE 1699.374 Term Coefficient 95% CI SE t statistic DF p Intercept -9154 -23045 to 4737 6234.4 -1.47 10 0.1728 Commercial Banking 7.901 -39.748 to 55.550 21.3851 0.37 10 0.7195 Savings Institutions 8.797 -13.315 to 30.908 9.9237 0.89 10 0.3962 Credit Unions -100.2 -301.3 to 101.0 90.28 -1.11 10 0.2932 Government sponsored -2.618 -30.477 to 25.241 12.5034 -0.21 10 0.8383 Agency and Government Sponsored 9.434 -4.832 to 23.700 6.4027 1.47 10 0.1714 Asset Backed -3.34 -20.37 to 13.69 7.644 -0.44 10 0.6714 Finance Corporation -24.58 -71.01 to 21.85 20.838 -1.18 10 0.2655 Non Agency MBS (B) 11.34 -5.62 to 28.30 7.613 1.49 10 0.1673 Total U.S. Gov MBS (B) -3 -9 to 3 2.7 -1.10 10 0.2956 S&P 500 R2 0.79 Adjusted R2 0.60 SE 243.213 Term Coefficient 95% CI SE t statistic DF p Intercept -837.7 -2825.8 to 1150.4 892.26 -0.94 10 0.3699 Commercial Banking 0.7569 -6.0626 to 7.5764 3.06061 0.25 10 0.8097 Savings Institutions 0.8265 -2.3381 to 3.9911 1.42027 0.58 10 0.5735 Credit Unions -7.911 -36.701 to 20.879 12.9210 -0.61 10 0.5540 Government sponsored -0.5208 -4.5080 to 3.4664 1.78947 -0.29 10 0.7770 Agency and Government Sponsored 1.087 -0.955 to 3.128 0.9164 1.19 10 0.2631 Asset Backed -0.68 -3.12 to 1.76 1.094 -0.62 10 0.5482 Finance Corporation -3.472 -10.117 to 3.173 2.9823 -1.16 10 0.2713 Non Agency MBS (B) 1.876 -0.551 to 4.304 1.0896 1.72 10 0.1158 Total U.S. Gov MBS (B) -0.4876 -1.3542 to 0.3791 0.38897 -1.25 10 0.2385 NYSE R2 0.89 Adjusted R2 0.80 SE 1041.118 Term Coefficient 95% CI SE t statistic DF p Intercept -3636 -12146 to 4875 3819.5 -0.95 10 0.3636 Commercial Banking 4.888 -24.304 to 34.080 13.1015 0.37 10 0.7169 Savings Institutions 3.411 -10.136 to 16.957 6.0797 0.56 10 0.5872 Credit Unions -37.9 -161.1 to 85.3 55.31 -0.69 10 0.5088 Government sponsored -1.798 -18.866 to 15.270 7.6602 -0.23 10 0.8191 Agency and Government Sponsored 4.823 -3.917 to 13.563 3.9226 1.23 10 0.2470 Asset Backed -2.759 -13.193 to 7.675 4.6830 -0.59 10 0.5688 Finance Corporation -18.36 -46.80 to 10.09 12.766 -1.44 10 0.1810 Non Agency MBS (B) 9.863 -0.529 to 20.255 4.6640 2.11 10 0.0606 Total U.S. Gov MBS (B) -2.265 -5.975 to 1.445 1.6650 -1.36 10 0.2036 NASDAQ R2 0.59 Adjusted R2 0.22 SE 792.941 Term Coefficient 95% CI SE t statistic DF p Intercept -1534 -8015 to 4948 2909.0 -0.53 10 0.6096 Commercial Banking 0.6868 -21.5465 to 22.9202 9.97844 0.07 10 0.9465 Savings Institutions 0.9137 -9.4037 to 11.2310 4.63046 0.20 10 0.8475 Credit Unions -7.779 -101.642 to 86.084 42.1261 -0.18 10 0.8572 Government sponsored -1.026 -14.025 to 11.973 5.8342 -0.18 10 0.8639 Agency and Government Sponsored 2.488 -4.169 to 9.145 2.9876 0.83 10 0.4244 Asset Backed -1.98 -9.93 to 5.97 3.567 -0.56 10 0.5910 Finance Corporation -8.576 -30.240 to 13.088 9.7230 -0.88 10 0.3985 Non Agency MBS (B) 4.971 -2.944 to 12.886 3.5522 1.40 10 0.1920 Total U.S. Gov MBS (B) -1.314 -4.139 to 1.512 1.2681 -1.04 10 0.3246 The mortgage model results when compared against the markets are mixed. Although the four models are not super strong in predicting outcomes (NYSE best and NASDAQ is the worst – by R² data), the models show that there is an inverse relationship between Non Agency MBS and Total U.S. Gov. MSB. Non Agency MBS are those toxic Mortgage Backed Securities (MBS) assets issued by private sector companies and Total U.S. Gov. MBS are those toxic mortgage backed securities issued by government entities Fannie Mae, Ginnie Mae, and Freddie Mac. Non Agency MBS work to drive up market values whereas; Total U.S. Gov. MBS work to drive market values lower. It is not all bad news for the government since agency and government sponsored mortgages drive up market values, but government sponsored mortgages drive down the market. I am not sure of the difference between these two, but I suspect they include VA, FHA, and GSE mortgage loans. Unfortunately, I am not sure how this data is divided between the two programs. Commercial banks and savings institution mortgages tend to drive the market up; whereas credit union and finance corporation mortgages drive the market value down. However, it is important to note that the correlation is strongest between Non Agency MBS and Total U.S. Gov. MBS with the markets than the other variables (based on higher t statistic and lower p results). Here are the model results for the economic indicators when modeled against commercial banks, saving institutions, credit unions, government sponsored, agency and government sponsored, asset backed, finance corporations, non-agency MBS, and total U.S. government MBS mortgages. Foreclosure Rate R2 0.99 Adjusted R2 0.98 SE 0.087 Term Coefficient 95% CI SE t statistic DF p Intercept 0.8404 -0.4088 to 2.0896 0.55222 1.52 9 0.1624 Commercial Banking -0.001989 -0.004591 to 0.000613 0.0011501 -1.73 9 0.1178 Savings Institutions 0.0006746 -0.0014666 to 0.0028159 0.00094655 0.71 9 0.4941 Credit Unions 0.009413 -0.008600 to 0.027427 0.0079630 1.18 9 0.2674 Government sponsored -0.0006434 -0.0022507 to 0.0009639 0.00071051 -0.91 9 0.3888 Agency and Government Sponsored 0.0003073 -0.0006934 to 0.0013079 0.00044233 0.69 9 0.5048 Asset Backed 0.001188 -0.000392 to 0.002768 0.0006984 1.70 9 0.1231 Finance Corporation -0.0007006 -0.0031269 to 0.0017257 0.00107256 -0.65 9 0.5299 Non Agency MBS (B) -0.001926 -0.003387 to -0.000465 0.0006458 -2.98 9 0.0154 Total U.S. Gov MBS (B) 0.0004141 0.0000832 to 0.0007451 0.00014629 2.83 9 0.0197 Home Ownership Rate R2 0.96 Adjusted R2 0.93 SE 0.5043 Term Coefficient 95% CI SE t statistic DF p Intercept 57.73 53.61 to 61.85 1.850 31.20 10 <0.0001 Commercial Banking 0.000874 -0.013267 to 0.015015 0.0063465 0.14 10 0.8932 Savings Institutions 0.005253 -0.001309 to 0.011815 0.0029451 1.78 10 0.1048 Credit Unions -0.04506 -0.10476 to 0.01464 0.026793 -1.68 10 0.1235 Government sponsored 0.0001896 -0.0080783 to 0.0084576 0.00371069 0.05 10 0.9602 Agency and Government Sponsored 0.004247 0.000013 to 0.008480 0.0019002 2.23 10 0.0494 Asset Backed -0.001434 -0.006488 to 0.003621 0.0022685 -0.63 10 0.5416 Finance Corporation 0.004906 -0.008873 to 0.018685 0.0061841 0.79 10 0.4460 Non Agency MBS (B) 0.001022 -0.004012 to 0.006056 0.0022593 0.45 10 0.6606 Total U.S. Gov MBS (B) -0.001057 -0.002855 to 0.000740 0.0008066 -1.31 10 0.2192 Home Vacancy Rate R2 0.98 Adjusted R2 0.96 SE 0.0846 Term Coefficient 95% CI SE t statistic DF p Intercept 1.161 0.470 to 1.853 0.3103 3.74 10 0.0038 Commercial Banking 0.001398 -0.000973 to 0.003770 0.0010643 1.31 10 0.2183 Savings Institutions 0.00136 0.00026 to 0.00246 0.000494 2.75 10 0.0204 Credit Unions 0.001868 -0.008143 to 0.011879 0.0044930 0.42 10 0.6863 Government sponsored -0.001912 -0.003298 to -0.000525 0.0006223 -3.07 10 0.0118 Agency and Government Sponsored -0.0006303 -0.0013403 to 0.0000797 0.00031864 -1.98 10 0.0761 Asset Backed 0.0006792 -0.0001684 to 0.0015268 0.00038041 1.79 10 0.1045 Finance Corporation -0.002279 -0.004590 to 0.000032 0.0010370 -2.20 10 0.0527 Non Agency MBS (B) -0.0009889 -0.0018330 to -0.0001447 0.00037887 -2.61 10 0.0260 Total U.S. Gov MBS (B) 0.0005488 0.0002475 to 0.0008502 0.00013525 4.06 10 0.0023 Mortgage Delinquency Rate R2 0.93 Adjusted R2 0.87 SE 0.239 Term Coefficient 95% CI SE t statistic DF p Intercept 3.886 0.459 to 7.313 1.5147 2.57 9 0.0304 Commercial Banking -0.003408 -0.010544 to 0.003729 0.0031548 -1.08 9 0.3082 Savings Institutions 0.003294 -0.002580 to 0.009167 0.0025964 1.27 9 0.2364 Credit Unions 0.007254 -0.042158 to 0.056665 0.0218426 0.33 9 0.7474 Government sponsored -0.002348 -0.006757 to 0.002061 0.0019489 -1.20 9 0.2590 Agency and Government Sponsored 6.1360E-05 -2.6834E-03 to 2.8061E-03 1.2133E-003 0.05 9 0.9608 Asset Backed 0.002569 -0.001765 to 0.006903 0.0019158 1.34 9 0.2128 Finance Corporation 0.002907 -0.003748 to 0.009563 0.0029420 0.99 9 0.3489 Non Agency MBS (B) -0.004429 -0.008436 to -0.000421 0.0017715 -2.50 9 0.0339 Total U.S. Gov MBS (B) 0.001141 0.000233 to 0.002049 0.0004013 2.84 9 0.0193 These are strong predictions models with high R² values and they are consistent with the results in Part I of this blog. The strongest correlation exists between Non Agency MBS and Total U.S. Gov. MBS with the economic indicators. While Non Agency MBS works to drive down foreclosure rates, home vacancy rates, delinquency rates, and increase home ownership rates; Total U.S. Gov. MBS works to do the opposite. This would lead me to believe that toxic mortgage assets from the government were the prime culprit in the meltdown and not so much those toxic assets from the private sector. In these economic indicator models savings institutions and credit union mortgages had a positive effect on economic variables; whereas commercial banking and finance corporations were mostly negative. The results for government sponsored and agency and government sponsored mortgages were mixed (in some cases improved and in other cases decreased results) on the economic indicators.