Answer all questions (including all sub-questions). Make sure that your final answers are clear and legible. When discussing results, please make sure to include properly formatted regression tables.
Data/.Rhistory View(dta) Data/Ch8_Exercise1_Presidential_approval.dta __MACOSX/Data/._Ch8_Exercise1_Presidential_approval.dta Data/Ch8_Exercise1_Presidential_approval.RData Data/Ch8_Exercise1_Presidential_approval.RData __MACOSX/Data/._Ch8_Exercise1_Presidential_approval.RData Data/Ch8_Exercise2_Peace_Corps.RData Data/Ch8_Exercise2_Peace_Corps.RData __MACOSX/Data/._Ch8_Exercise2_Peace_Corps.RData Data/Ch8_Exercise4_HOPE_scholarship.dta __MACOSX/Data/._Ch8_Exercise4_HOPE_scholarship.dta Data/Ch8_Exercise4_HOPE_scholarship.RData Data/Ch8_Exercise4_HOPE_scholarship.RData __MACOSX/Data/._Ch8_Exercise4_HOPE_scholarship.RData PLCY 611: Problem Set #4 Due Date: November 15th, 2021 at 11:59PM Instructions: Answer all questions (including all sub-questions).Make sure that your final answers are clear and legible.. When discussing results, please make sure to include properly formatted regression tables. Problem 1 [30 Pts] Researchers have long been interested in the relationship between economic factors and presidential elec- tions. The data set (Ch8 Exercise1 Presidential approval ) includes data on presidential approval polls and unemployment rates by state over a number of years, as described in table 8.8 of your textbook. 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. [6 pts] b. Many political observers believe politics in the South are different. Add South as an additional inde- pendent variable and reestimate the model from part (a). Report the estimated regression equation. Do the results change? [6 pts] 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? [6 pts] 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. [6 pts] e. Estimate a two-way fixed effects model. How does this model affect the results? [6 pts] ( 1 ) Problem 2 [30 Pts] How do young people respond to economic conditions? Are they more likely to pursue public service when jobs are scarce? To get at this question, we will analyze data (Ch8 Exercise2 Peace Corps) on state economies and applications to the Peace Corps, as described in table 8.9. a. Before looking at the data, what relationship do you hypothesize between these two variables? Explain your hypothesis. [5 pts] b. Run a pooled regression of Peace Corps applicants per capita on the state unemployment rate and year dummies. Describe and critique the results. [5 pts] c. Plot the relationship between the unemployment and Peace Corps applications with the state of the observation labelled. What sticks out? How may this impact the estimate on unemployment rate in the pooled regression above? Create a scatterplot without the outlier and comment briefly on the difference. [5 pts] d. Run the pooled model from above without the outlier. Comment briefly on the results. [5 pts] e. Run a two-way fixed effect model without the outlier using the LSDV approach. Do your results change from the pooled analysis? Which results are preferable? [5 pts] f. Run a two-way fixed effect model without the outlier using the fixed effects command in Stata or R. Compare to LSDV results. [5 pts] Problem 3 [30 Pts] In 1993, Georgia initiated a HOPE scholarship program to let state residents who had at least a B average in high school attend public college in Georgia for free. The program is not need based. Did the program increase college enrollment? Or did it simply transfer funds to families who would have sent their children to college anyway? Dynarski (2000) used data (Ch8 Exercise4 HOPE scholarship) on young people in Georgia and neighboring states to assess this question. Table 8.11 lists the variables. a. Run a basic difference-in-difference model. What is the effect of the program? [5 pts] b. Calculate the percentage of people in the sample in college from the following four groups: (i) Before 1993/non-Georgia, (ii) Before 1993/Georgia, (iii) After 1992/non-Georgia, (iv) After 1992/Georgia. First, use the mean function. Second, use the coefficients from the OLS output in part (a). [5 pts] c. Graph the fitted lines for the Georgia group and non-Georgia samples. [5 pts] d. Use panel data formulation for a difference-in-difference model to control for all year and state effects. [5 pts] e. Add covariates for 18-year-olds and African-Americans to the panel data formulation. What is the effect of the HOPE program? [5 pts] f. The way the program was designed, Georgia high school graduates with a B or higher average and annual family income over $50,000 could qualify for HOPE by filling out a simple one-page form. Those with lower income were required to apply for federal aid with a complex four-page form and had any federal aid deducted from their HOPE scholarship. Run separate basic difference-in-difference models for these two groups and comment on the substantive implication of the results. [5 pts] Problem 4 [10 Pts] For this question, you will need to go to the FRED (Federal Reserve Economic Data) website here and download GDP data from January 1, 1947 to July 1, 2021. In the search bar of the FRED website, type “GDP,” press enter, and click on the first option (which should be “Gross Domestic Product” Billions of Dollars, Quarterly, Seasonally Adjusted Annual Rate). Download the dataset and load it into R to answer the following questions: a. Does this time-series data exhibit autocorrelation (in answering this question, make sure to include and discuss the ACF figure)? [5 pts] b. Convert the GDP data into a percent change (before doing this, make sure that your data is time sorted). Does this new variable exhibit autocorrelation (again, show and discus the ACF graph). [5 pts]