Discrete Choice Analysis

This lecture covers the integration of machine learning capabilities into discrete choice models, focusing on the incorporation of latent variables to enhance model flexibility and interpretability. The instructor discusses the methodology of combining structural modeling with data-driven approaches, emphasizing the importance of theory constraints in model estimation. Various examples and research trends in the field of discrete choice analysis are explored, highlighting the shift towards hybrid models that combine machine learning and econometric techniques. Statistical properties, hypothesis testing, and model selection criteria are also discussed, providing a comprehensive overview of the evolving landscape of choice modeling.