![]() Using the Base Model, conduct the F-test that all variables have no effect.Then calculate the F-statistic and (ball-park or calculate precisely) the p-value. Rather, estimate an unrestricted model and a restricted model to obtain the sum of squared residuals. Using the Base Model, again test the claim that the return to each additional year of schooling is nine percent, but this time do not use Stata's test command.Test the claim that the variable age does not belong in the model.Test the claim that the gender differential is ten percent.Test the claim that each year of additional schooling increases expected wages by nine percent.Test the claim that there is no effect of race of wages.Test the claim that there is no difference between being black or hispanic on wages.Re-estimate the Base Model, and then do the following.Describe the predicted relationship between age and ln(wages) as completely as possible. Explain why the residuals might give one more confidence in this model over the model in step 3. Plot the residuals from the regression against age. Summarize the residuals of the regression. Regress logged wages on age, age squared, years of schooling, race (omit white), and sex (omit male).Given what you know about wages, do the results generally make sense? Explain why the residuals should make one question the model specification. Regress wages on age, age squared, years of schooling, race (omit white), and sex (omit male).Provide the summary statistics for this data set. Keep (and order) wage, lnwage, age, age2, school, hispanic, black, white, female, and male. Also create age2 to equal the square of age, and create lnwage to equal the natural log of annual earnings. Create dummy variables for race (hispanic, black, and white) and sex (female and male).Separately tabulate race, school, and sex. Describe and summarize the data to better understand the data.And following that, a Stata program is included that would execute the commands for all 10 questions. If you get stuck, however, all 10 questions with Stata commands are repeated below. To best learn, try to work through all 10 questions by providing Stata commands and answers. ![]() The variables are race (1=hispanic, 2=black, 3=white), age, school (years of schooling), sex (F=female, M=male), and annual labor income. The data set contains five variables on 704 individuals. ![]() The data for this problem are in Stata format: wages.dta. A second thought is to run -update all- to make sure your Stata installation is up to date and correctly synchronized between the executable and ado files.Stata Lab 5: Testing Coefficients Stata Lab 5: Testing Coefficients Perhaps you are getting some error messages that would be helpful to see. My first suggestion is to try adding the -verbose- option to the -runby- command so you can see what is happening inside program one_regression. Time count errors no-data processed saved remaining runby one_regression, by(Countr圜ode Industr圜lass Year) statusĮlapsed - by-groups - observations - time ![]() Please let me know what you think/suggest.Ĭode. Input str4 Countr圜ode float(Year Industr圜lass Y X) ![]()
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