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OLS and OLS (Robust Regression Analysis) with STATA

Heteroskedasticity Diagnostics and Corrections

For this exercise, use newschools9810.dta. Please download the do-file for this assignment, "Class 8 Exercise 2014.do," from the course website, and perform all the required statistical operations as directed below.

Please submit:
a) A printed Stata log file documenting that you've completed each of the operations directed below as well as results. As always, be sure to run only your corrected Do file so your printed log file does not contain errors.
b) A printed write-up, which includes typed answers to the questions below.

1. Estimate an OLS regression with total expenditures per pupil as the dependent variable. Use the following independent variables:
• percent black
• percent free lunch
• total enrollment
• total enrollment squared
• percent Hispanic
• percent Asian
• percent full-time special education students
• percent immigrant
• percent female
• math z score
• year dummies
• a middle school dummy
• borough dummies

2. Graph the residuals versus total enrollment.

a. Copy and paste the graph into your typed write-up for submission.

b. What does the graph suggest about the possibility of heteroskedasticity?

3. Re-estimate the equation in 1, and perform a White test using estat imtest with the white option.

a. Copy and paste the results of the White test into your typed write-up for submission.

b. Is there heteroskedasticity? How do you know?

4. Re-estimate the equation in 1 using robust standard errors. How do the standard errors estimated with the model in this question differ from the standard errors estimated with the model in question 1? Is this what you expected? Why?


Solution Summary

This solution is comprised of a detailed explanation Regression Analysis in STATA. This solution mainly discussed the different regression models with actual variable, dummy variables and other transformation of variables. This solution explained the questions with interpretation of Regression Output in different models especially discussing the assumptions of regression model in terms of Heteroskedasticity and associated tests.