Linear Regression Model

This lecture covers the basics of the linear regression model, assumptions, properties of OLS estimator, hypothesis testing, normality assumption, t-tests, confidence intervals, p-values, and joint tests of coefficients. It also discusses the interpretation of the linear model, log transformations, consistency, asymptotic normality, and the impact of multicollinearity. Examples and practical considerations are provided, such as the choice of base category in regression analysis and dealing with log transformations of variables. The lecture emphasizes the importance of understanding the assumptions and implications of OLS in econometrics.