Statistical Science

Shape Constraints in Economics and Operations Research

Andrew L. Johnson and Daniel R. Jiang

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Abstract

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.

Article information

Source
Statist. Sci., Volume 33, Number 4 (2018), 527-546.

Dates
First available in Project Euclid: 29 November 2018

Permanent link to this document
https://projecteuclid.org/euclid.ss/1543482057

Digital Object Identifier
doi:10.1214/18-STS672

Mathematical Reviews number (MathSciNet)
MR3881207

Zentralblatt MATH identifier
07032828

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

Citation

Johnson, Andrew L.; Jiang, Daniel R. Shape Constraints in Economics and Operations Research. Statist. Sci. 33 (2018), no. 4, 527--546. doi:10.1214/18-STS672. https://projecteuclid.org/euclid.ss/1543482057


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