Answer: Empirical Exercise 8.2(l) involves running a regression to estimate the relationship between two variables of interest. In evaluating the internal validity of the regression used to answer this exercise, we need to consider several potential sources of bias and other issues that could affect the accuracy of the estimated relationship.
Omitted variable bias: This occurs when a relevant variable is left out of the regression model. If this happens, the estimated coefficients may be biased and inconsistent. For example, if we are estimating the effect of education on earnings but fail to include information about work experience, the coefficient on education may be biased upwards.
Misspecification of the functional form of the regression: If the relationship between the dependent variable and the independent variable(s) is nonlinear, we may need to use a different functional form of the regression equation to obtain accurate estimates. If we fail to do so, the estimated coefficients may be biased and inconsistent.
Errors in variables: When measurement errors occur in the independent variable(s), this can cause the estimated coefficients to be biased and inconsistent.
Sample selection: If the sample used to estimate the regression equation is not representative of the population of interest, then the estimated coefficients may be biased and inconsistent.
Simultaneous causality: When there is a two-way causal relationship between the dependent variable and the independent variable(s), it may not be possible to determine the direction of causality from the regression coefficients.
Inconsistency of the OLS standard errors: If there is heteroskedasticity or autocorrelation in the data, then the ordinary least squares (OLS) standard errors may be inconsistent. This can lead to incorrect conclusions about the statistical significance of the estimated coefficients.
To address these issues, researchers should take care to select an appropriate sample, use a suitable functional form of the regression equation, and account for any relevant variables that could be omitted or measured with errors. It may also be necessary to use alternative statistical techniques to account for simultaneous causality or heteroskedasticity in the data. Finally, researchers should always test the robustness of their results to different model specifications and sensitivity to potential sources of bias.
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