Autocorrelation and Instrumental Variables

This lecture covers the concepts of autocorrelation in error terms, focusing on first-order autocorrelation and its implications in econometrics. It discusses the consequences of autocorrelation, strategies for dealing with it, and tests such as the Breusch-Godfrey and Durbin-Watson tests. Additionally, it introduces the topic of instrumental variables, highlighting the importance of OLS assumptions and the impact of omitted variable bias. The lecture emphasizes the challenges posed by measurement error, omitted variable bias, and simultaneity in regression analysis, providing insights into how these issues can affect the consistency and validity of regression results.