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Maximum score estimation with nonparametrically generated regressors

Authors: Le-Yu Chen , Sokbae Lee and Myung Jae Sung
Date: 28 May 2014
Type: cemmap Working Paper, CWP27/14
DOI: 10.1920/wp.cem.2014.2714

Abstract

The estimation problem in this paper is motivated by 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 fi…rst stage and then the preference parameters in the second stage based on Manski (1975, 1985)’s maximum score estimator using the choice data and …first stage estimates. This setting can be extended to maximum score estimation with nonparametrically generated regressors. The paper establishes consistency and derives rate of convergence of the two-stage maximum score estimator. Moreover, the paper also provides 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. The paper also presents some Monte Carlo simulation results for …finite-sample behavior of the two-stage estimator.

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Now published:
Le-Yu Chen, Sokbae Lee and Myung Jae Sung October 2014, Maximum score estimation with nonparametrically generated regressors, Journal Article, Wiley
Previous version:
Le-Yu Chen, Sokbae Lee and Myung Jae Sung April 2013, Maximum score estimation of preference parameters for a binary choice model under uncertainty, cemmap Working Paper, CWP14/13, Cemmap

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