ch woodridge异方差性第8计量济学导论现代方法.pdfVIP

Chapter 8 Multiple Regression ysis: Heteroscedasticity 1. Consequences of Heteroskedasticity for OLS 2. Heteroskedasticity-Robust Inference after OLS Estimation 3. Testing for Heteroskedasticity 4. Weighted Least Squares Estimation 5. The Linear Probability Model Revisited Assignments: Promblems 7 Computer Exercises C2, C5, C9, C11 Chapter 8 Multiple Regression ysis: Heteroscedasticity 8.1 Consequences of Heteroskedasticity for OLS Unaffected properties: OLS still unbiased and consistent under heteroscedastictiy! Also, interpretation of R-squared is not changed. Unconditional error variance is unaffected by heteroscedasticity (which refers to the conditional error variance) Affected properties: Heteroscedasticity invalidates variance formulas for OLS estimators. The usual F-tests and t-tests are not valid under heteroscedasticity. Under heteroscedasticity, OLS is no longer the best linear unbiased estimator (BLUE); there may be more efficient linear estimators. OLS is asymptotically efficient in the class of estimators. With relatively large sample sizes, it might no be so important to obtain an efficient estimator. Chapter End Chapter 8 Multiple Regression ysis: Heteroscedasticity 8.2 Heteroskedasticity-Robust Inference after OLS Estimation (1/5) Formulas for OLS standard errors and related statistics have been developed that are robust to heteroscedasticity of unknown form All formulas are only valid in large samples Formula for heteroscedasticity-robust OLS standard error Using these formulas, the usual t- valid asymptotically The usual F-statistic does not work u