Researchers have long been interested in the relationship between economic factors and presidential elections. The PresApproval.dta data set includes data on presidential approval polls and unemployment rates by state over a number of years. Table 8.8 lists the variables.

(a) Use pooled data for all years to estimate a pooled OLS regression explaining presidential approval as a function of state unemployment rate. Report the estimated regression equation, and interpret the results.

(b) Many political observers believe politics in the South are different. Add South as an additional independent variable, and reestimate the model from part (a). Report the estimated regression equation. Do the results change?

(c) Reestimate the model from part (b), controlling for state fixed effects by using the de-meaned approach. How does this approach affect the results? What happens to the South variable in this model? Why? Does this model control for differences between southern and other states?

(d) Reestimate the model from part (c) controlling for state fixed effects using the LSDV approach. (Do not include a South dummy variable). Compare the coefficients and standard errors for the unemployment variable.

(e) Estimate a two-way fixed effects model. How does this model affect the results?

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TABLE 8.8 Variables for Presidential Approval Data Variable name State StCode Year Description State name State numeric ID Year PresApprov UnemPet South Percent positive presidential approval State unemployment rate Southern state (1 = yes, 0 = no)