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In this exercise, we will work with the data file ch8_cps. xlsx. The data is collected by the Bureau of Labor Statistics in the U.S. Department of Labor through a survey known as Current Population Survey, and contain data on labor force characteristics of the population, including the level of employment, unemployment, and earnings. Approximately 60.000 randomly selected U.S. households are surveyed each month. The sample is chosen by randomly selecting addresses from a database comprised of addresses from the most recent decennial census augmented with data on new housing units constructed after the last census. Our data are from the March 2016 survey, and include the following variables. To import data and to get summary statistics, use the following code chunk: stargazer (as . data. frame (mydata) , type = "text", title="Summary Statistics") The following table provides summary statistics for each variable. TABLE 1. Summary Statistics (a) Consider ahe =0+1 educ +2 female +u. Estimate this regression by assuming that u is heteroskedastic. Show your estimation output. Test the following hypothesis H0:2=0 versus H1:2=0 at the 5% significance level. Construct a 95% confidence interval for 2 (b) Consider the following regression: ahe=0+1educ+2female+3age Estimate this regression by assuming that u is heteroskedastic. Show your estimation output. Is age an important determinant of earnings? Use an appropriate statistical test and/or confidence interval to explain your answer. (c) Consider the following regression: ahe=0+1educ+2female+3age+4northeastu+5midwest+6south+u Estimate this model by assuming that u is heteroskedastic. Show your estimation output. Do there appear to be important regional differences? That is, test the following hypotheses at the 5% significance level: H0:4=0,5=0,6=0, H1: At least one coefficient is non-zero. (d) Consider the model in Part (c). Test H0:1=3 versus H1:1=3 at the 5% significance level

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