Nonparametric estimation and inference under shape restrictions

Joel L. Horowitz and Sokbae (Simon) Lee

No 67/15, CeMMAP working papers from Institute for Fiscal Studies

Abstract: Economic theory often provides shape restrictions on functions of interest in applications, such as monotonicity, convexity, non-increasing (non-decreasing) returns to scale, or the Slutsky inequality of consumer theory; but economic theory does not provide finite-dimensional parametric models. This motivates nonparametric estimation under shape restrictions. Nonparametric estimates are often very noisy. Shape restrictions stabilize nonparametric estimates without imposing arbitrary restrictions, such as additivity or a single-index structure, that may be inconsistent with economic theory and the data. This paper explains how to estimate and obtain an asymptotic uniform confidence band for a conditional mean function under possibly nonlinear shape restrictions, such as the Slutsky inequality. The results of Monte Carlo experiments illustrate the finite-sample performance of the method, and an empirical example illustrates its use in an application.

Date: 2015-10-19
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.cemmap.ac.uk/wp-content/uploads/2020/08/CWP6715.pdf (application/pdf)

Related works:
Journal Article: Nonparametric estimation and inference under shape restrictions (2017)
Working Paper: Nonparametric estimation and inference under shape restrictions (2016)
Working Paper: Nonparametric estimation and inference under shape restrictions (2016)
Working Paper: Nonparametric estimation and inference under shape restrictions (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:azt:cemmap:67/15

DOI: 10.1920/wp.cem.2015.6715

Access Statistics for this paper

More papers in CeMMAP working papers from Institute for Fiscal Studies Contact information at EDIRC.
Bibliographic data for series maintained by Dermot Watson (cemmap.repec@ifs.org.uk).