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)
Tuesday, July 26, 2011
Sunday, July 17, 2011
State Social Benefits Model
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)
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)
Monday, July 11, 2011
State Budget Model
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)
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)
Tuesday, July 5, 2011
Federal Trade Deficit Model (Updated)
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
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
Monday, July 4, 2011
Consumer Spending Model
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)
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)
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