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Maximum score estimation with nonparametrically generated regressors

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  • Le-Yu Chen
  • Sokbae (Simon) Lee
  • Myung Jae Sung

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 inthe 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.

Suggested Citation

  • Le-Yu Chen & Sokbae (Simon) Lee & Myung Jae Sung, 2014. "Maximum score estimation with nonparametrically generated regressors," CeMMAP working papers 27/14, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:27/14
    DOI: 10.1920/wp.cem.2014.2714
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