Shape Constraints in Economics and Operations Research

作     者：Johnson, Andrew L. Jiang, Daniel R. 

作者机构：Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77840 USA Osaka Univ Sch Informat Sci & Technol Suita Osaka 5650871 Japan Univ Pittsburgh Dept Ind Engn Pittsburgh PA 15261 USA 

出 版 物：《STATISTICAL SCIENCE》 (统计科学)

年 卷 期：2018年第33卷第4期

页      面：527-546页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Osaka University 

主　　题：Shape constraints multivariate convex regression nonparametric regression production economics consumer preferences revealed preferences approximate dynamic programming reinforcement learning 

摘      要：Shape constraints, motivated by either application-specific assumptions or existing theory, can be imposed during model estimation to restrict the feasible region of the parameters. Although such restrictions may not provide any benefits in an asymptotic analysis, they often improve finite sample performance of statistical estimators and the computational efficiency of finding near-optimal control policies. This paper briefly reviews an illustrative set of research utilizing shape constraints in the economics and operations research literature. We highlight the methodological innovations and applications, with a particular emphasis on utility functions, production economics and sequential decision making applications.