Economics at your fingertips  

Constructive identification in some nonseparable discrete choice models

Rosa L. Matzkin

Journal of Econometrics, 2019, vol. 211, issue 1, 83-103

Abstract: This paper introduces new results on the nonparametric identification of separable and nonseparable discrete choice models. It presents constructive methods for recovering the derivatives of the utility functions of the alternatives in a set, when these utility functions are nonparametric and nonseparable in unobservable random terms. When the utility functions are separable, the constructive methods require fewer assumptions. It is assumed that only the probability of choosing one alternative outside the set is observed. The conditions for identification involve testable shape restrictions on the distributions of the nonseparable unobservable random terms.

Keywords: Discrete choice; Nonparametric identification; Unobserved heterogeneity; Random utility; Random coefficients; Nonseparability; Monotonicity; Rank conditions; Distribution-free methods (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
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:

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-08-17
Handle: RePEc:eee:econom:v:211:y:2019:i:1:p:83-103