Optimal Transport Methods in Economics (Hardcover)

Optimal Transport Methods in Economics is the first textbook on the subject written especially for students and researchers in economics. Optimal transport theory is used widely to solve problems in mathematics and some areas of the sciences, but it can also be used to understand a range of problems in applied economics, such as the matching between job seekers and jobs, the determinants of real estate prices, and the formation of matrimonial unions. This is the first text to develop clear applications of optimal transport to economic modeling, statistics, and econometrics. It covers the basic results of the theory as well as their relations to linear programming, network flow problems, convex analysis, and computational geometry. Emphasizing computational methods, it also includes programming examples that provide details on implementation. Applications include discrete choice models, models of differential demand, and quantile-based statistical estimation methods, as well as asset pricing models. Authoritative and accessible, Optimal Transport Methods in Economics also features numerous exercises throughout that help you develop your mathematical agility, deepen your computational skills, and strengthen your economic intuition. * The first introduction to the subject written especially for economists* Includes programming examples* Features numerous exercises throughout* Ideal for students and researchers alike

General

Imprint:	Princeton University Press
Country of origin:	United States
Release date:	August 2016
First published:	2017
Authors:	Alfred Galichon
Dimensions:	235 x 152 x 21mm (L x W x T)
Format:	Hardcover - Trade binding
Pages:	184
ISBN-13:	978-0-691-17276-7
Categories:	Books > Business & Economics > Economics > Economic theory & philosophy Books > Business & Economics > Economics > Microeconomics > General Books > Business & Economics > Economics > Econometrics > General Books > Science & Mathematics > Mathematics > Optimization > Linear programming
LSN:	0-691-17276-5
Barcode:	9780691172767

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