A fundamental work on discrete choice models, with an emphasis on random utility models. Discrete choice models are essential tools for understanding decision-making processes when individuals must choose between alternatives. They have applications across a wide range of social sciences, especially economics, marketing, and political science. This book offers a foundational approach to discrete choice models, introducing the logit model and its extensions, logistic and Pseudo-Maximum Likelihood analyses, as well as generalized linear models, and demonstrating their use in analyzing important econometric models. These include international trade gravity models, demand estimation, identification with and without transport, hedonic markets, and dynamic discrete choice. Bridging theoretical clarity and practical application, the book is suitable for use in graduate courses and will be an essential resource for researchers and practitioners. • Extensive coverage of computational topics, focusing on optimization and reformulation as generalized linear models • Emphasis on econometric issues, including simulation methods, estimation, and inference, with estimation techniques based on simulated and real datasets • A significant collection of exercises and problems at the end of each chapter • Two appendices, one covering the mathematical tools needed to understand the material, and the other providing Python code examples.
