Name: Discrete Choice Models. Mathematical Methods, Econometrics, and Data Science
SKU: 64847337
Price: 2003.00 TWD
Availability: InStock
Author: Alfred Galichon
ISBN: 9780691221809

portada Discrete Choice Models. Mathematical Methods, Econometrics, and Data Science

Discrete Choice Models. Mathematical Methods, Econometrics, and Data Science

Type

Physical Book

Publisher

Princeton University Press

Author

Alfred Galichon

Year

2026

Pages

288

Format

Hardcover

Dimensions

23.5x15.6 cm

ISBN13

9780691221809

Discrete Choice Models. Mathematical Methods, Econometrics, and Data Science

Alfred Galichon (Author) · Princeton University Press · Hardcover

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Synopsis "Discrete Choice Models. Mathematical Methods, Econometrics, and Data Science"

A foundational treatment of discrete choice models, with a focus on random utility modelsDiscrete choice models are essential tools for understanding decision-making when individuals must choose among alternatives. They have applications across the social sciences, notably in economics, marketing, and political science. This book offers a foundational treatment of discrete choice models, introducing the logit model and its generalizations, logistic and Poisson regressions, and generalized linear models, and demonstrates their use in analyzing important econometric models. These include international trade gravity, demand estimation, matching with and without transfers, hedonic markets, and dynamic discrete choice. Bridging theoretical clarity and practical applicability, the book is suitable for use in graduate-level coursework and will be an essential resource for researchers and practitioners. • Extensive coverage of computational issues, focusing on optimization and the reformulation as generalized linear models• Emphasis on econometric questions, including simulation, estimation, and inference methods, with estimation techniques based on both simulated and actual datasets• Substantial set of exercises and problems at the end of each chapter• Two appendixes, with one covering the mathematical tools needed to understand the material, and the other the Python code examples