Maximum Likelihood Estimation: Econometrics

This lecture covers the concept of Maximum Likelihood Estimation (MLE) in econometrics, focusing on the principles, properties, and applications of MLE. It explains how MLE is used to estimate unknown parameters by maximizing the likelihood of observing the sample data. The lecture also delves into the intuition behind MLE, the likelihood function, the information matrix, and different types of specification tests such as the Wald test, likelihood ratio test, and Lagrange multiplier test. Additionally, it discusses the asymptotic properties of MLE, including consistency, asymptotic normality, and efficiency. The lecture concludes by comparing the three test types and explaining how MLE can be used as a Generalized Method of Moments (GMM) estimator.