PPT-Heteroscedasticity

What does it mean The variance of the error term is not constant What are its consequences Heteroscedasticity does not destroy the unbiasedness and consistency properties of OLS estimators. William Greene. Stern School of Business. New York University. Part 6. Modeling Heterogeneity. Several Types of Heterogeneity. Differences across choice makers. Observable: Usually demographics such as age, sex. . Violation of this assumption is not without consequences: if the level-1 variances are assumed to be equal but are really unequal, the point estimation of the level-2 coefficients will not be biased Professor William Greene. Stern School of Business. IOMS Department . Department of Economics. Inference and Regression. Part . 9 – Linear Model Topics. Agenda. Variable Selection – Stepwise Regression. Amine . Ouazad. Ass. Prof. of Economics. Outline. Heteroscedasticity. Clustering. Generalized . Least . Squares. For . heteroscedasticity. For autocorrelation. Heteroscedasticity. Issue. The issue arises whenever the residual’s variance depends on the observation, or depends on the value of the covariates.. Definition. Consequences of heteroscedasticity. Testing for . Heteroskedasticity. Breusch-Pagan. White test (2 forms). Fixing the problem. Robust standard errors. Weighted Least Squares. Fixing heteroskedasticity in LPM model..