MLE Applications: Binary Choice Models

This lecture explores the application of Maximum Likelihood Estimation (MLE) in binary choice models. MLE estimators use assumptions about the distribution of random variables to maximize the loglikelihood and select parameters most likely to have generated the data. The lecture covers the quasi-ML as a GMM estimator, asymptotic distribution calculations, probit and logit models, latent variable representation, and the estimation of covariance matrices. It delves into the challenges of modeling probabilities directly, interaction terms, goodness-of-fit measures, marginal effects, specification tests (Wald, likelihood ratio, LM), and concludes with insights on ordered outcomes in multiresponse models.