Heteroskedasticity and Autocorrelation

This lecture covers the concepts of heteroskedasticity and autocorrelation in econometrics. It starts by explaining the implications of heteroskedasticity on OLS estimators and the need for generalized least squares. The lecture then delves into practical applications, such as weighted least squares and heteroskedasticity-consistent standard errors. Additionally, it discusses testing methods for heteroskedasticity, including the Breusch-Pagan and White tests. The consequences of heteroskedasticity on hypothesis testing are explored, along with the importance of using heteroskedasticity-consistent estimators. The lecture concludes with a summary of the key points related to heteroskedasticity and introduces the topic of autocorrelation.