Linear Regression Basics

This lecture covers the fundamentals of linear regression, including Ordinary Least Squares (OLS) and the Linear Regression Model. It delves into topics such as the minimization of squared residuals, special cases like simple regression, and the Gauss-Markov assumptions. Additionally, it explores instrumental variables, heteroskedasticity, autocorrelation, and Generalized Method of Moments (GMM). The lecture also touches on Maximum Likelihood Estimation (MLE) theory, its properties, and test principles within the ML framework. Furthermore, it discusses applications of MLE, particularly in binary response models, and concludes with an overview of univariate time series analysis.