Maximum Likelihood Theory & Applications

This lecture covers the theory of maximum likelihood estimation, which involves making assumptions about the distribution of data to find the most plausible parameter estimates. It explores the method of moments, IV, OLS, and the concept of maximum likelihood. The instructor presents a simple example with red and yellow balls to illustrate the concept. The lecture delves into the intuition behind the maximum likelihood principle and the properties of maximum likelihood estimators. It also discusses applications of maximum likelihood estimation, such as binary choice models, count data models, and models for censored variables. The lecture concludes with a comparison of different hypothesis testing principles and the estimation of covariance matrices.