Journal ArticleDOI
Shape Constraints in Economics and Operations Research
TLDR
This paper briefly reviews an illustrative set of research utilizing shape constraints in the economics and operations research literature and highlights the methodological innovations and applications with a particular emphasis on utility functions, production economics and sequential decision making applications.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.read more
Citations
More filters
Journal ArticleDOI
Order Restricted Statistical Inference.
Posted Content
The collective model of household consumption: A nonparametric characterization
TL;DR: In this paper, the authors provide a nonparametric characterization of a general collective model for household consumption, which includes externalities and public consumption, and establish testable necessary and sufficient conditions for data consistency with collective rationality that only include observed price and quantity information.
Proceedings Article
Hard Shape-Constrained Kernel Machines
TL;DR: In this paper, the authors prove that hard affine shape constraints on function derivatives can be encoded in kernel machines, which represent one of the most flexible and powerful tools in machine learning and statistics.
Posted Content
Multivariate Convex Regression at Scale
Wenyu Chen,Rahul Mazumder +1 more
TL;DR: This framework can solve instances of the convex regression problem with $n=10^5$ and $d=10$---a QP with 10 billion variables---within minutes; and offers significant computational gains compared to current algorithms.
References
More filters
Book ChapterDOI
Chapter 42 Restrictions of economic theory in nonparametric methods
TL;DR: In this article, the authors describe several nonparametric estimation and testing methods for econometric models, which use the restrictions that can be derived from the model, such as concavity and monotonicity of functions, equality conditions, and exclusion restrictions.
Book ChapterDOI
Testable Restrictions on the Equilibrium Manifold
Donald J. Brown,Rosa L. Matzkin +1 more
TL;DR: In this paper, a finite system of polynomial inequalities in unobservable variables and market data that observations on market prices, individual incomes, and aggregate endowments must satisfy to be consistent with the equilibrium behavior of some pure trade economy is presented.
Report SeriesDOI
Best Nonparametric Bounds on Demand Responses
TL;DR: The authors used revealed preference inequalities to provide the tightest possible (best) nonparametric bounds on predicted consumer responses to price changes using consumer-level data over a finite set of relative price changes.
Journal ArticleDOI
On risk bounds in isotonic and other shape restricted regression problems
TL;DR: In this article, the authors consider the problem of estimating an unknown unknown estimator from noisy observations under the constraint that the estimator belongs to certain convex polyhedral cones in a polygonal space.
Posted Content
Restrictions of economic theory in nonparametric methods
TL;DR: In this paper, the authors describe several nonparametric estimation and testing methods for econometric models, which use the restrictions that can be derived from the model, such as concavity and monotonicity of functions, equality conditions, and exclusion restrictions.