Basic Econometric Models: Linear Regression
The basic tool for econometrics is the linear regression model. In modern econometrics, other statistical tools are frequently used, but linear regression is still the most frequently used starting point for an analysis. Estimating a linear regression on two variables can be visualized as fitting a line through data points representing paired values of the independent and dependent variables.
For example, consider Okun's law, which relates GDP growth to the unemployment rate. This relationship is represented in a linear regression where the change in unemployment rate is a function of an intercept, a given value of GNP growth multiplied by a slope coefficient and an error term, :
The unknown parameters and can be estimated. Here is estimated to be -1.77 and is estimated to be 0.83. This means that if GNP grew one point faster, the unemployment rate would be predicted to drop by .94 points (-1.77*1+0.83). The model could then be tested for statistical significance as to whether an increase in growth is associated with a decrease in the unemployment, as hypothesized. If the estimate of were not significantly different from 0, we would fail to find evidence that changes in the growth rate and unemployment rate were related.
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