centre for microdata methods and practice

ESRC centre

cemmap is an ESRC research centre

ESRC

Keep in touch

Subscribe to cemmap news

Maximum score estimation with nonparametrically generated regressors

Authors: Le-Yu Chen , Sokbae (Simon) Lee and Myung Jae Sung
Date: 01 October 2014
Type: Journal Article, Econometrics Journal, Vol. 17, No. 3, pp. 271–300
DOI: 10.1111/ectj.12034

Abstract

The estimation problem in this paper is motivated by the maximum score estimation of preference parameters in the binary choice model under uncertainty in which the decision rule is affected by conditional expectations. The preference parameters are estimated in two stages. We estimate conditional expectations nonparametrically in the first stage. Then, in the second stage, we estimate the preference parameters based on the maximum score estimator of Manski, using the choice data and first-stage estimates. This setting can be extended to maximum score estimation with nonparametrically generated regressors. In this paper, we establish consistency and derive the rate of convergence of the two-stage maximum score estimator. Moreover, we also provide sufficient conditions under which the two-stage estimator is asymptotically equivalent in distribution to the corresponding single-stage estimator that assumes the first-stage input is known. We also present some Monte Carlo simulation results for the finite-sample behaviour of the two-stage estimator.

Download full version
Previous version:
Le-Yu Chen, Sokbae (Simon) Lee and Myung Jae Sung May 2014, Maximum score estimation with nonparametrically generated regressors, cemmap Working Paper, CWP27/14, Institute for Fiscal Studies

Publications feeds

Subscribe to cemmap working papers via RSS

Search cemmap

Search by title, topic or name.

Contact cemmap

Centre for Microdata Methods and Practice

How to find us

Tel: +44 (0)20 7291 4800

E-mail us