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:
Adjusted R2 1.00
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.
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