Maximum score estimation of preference parameters for a binary choice model under uncertainty

Le-Yu Chen, Sokbae (Simon) Lee and Myung Jae Sung
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Myung Jae Sung: Institute for Fiscal Studies

No CWP14/13, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: This paper develops 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 and the preference parameters in the second stage based on Manski (1975, 1985)'s maximum score estimator using the choice data and first stage estimates. The paper establishes consistency and derives the rate of convergence of the corresponding two-stage estimator, which is of independent interest for maximum score estimation with generated regressors. The paper also provides results of some Monte Carlo experiments.

Keywords: discrete choice; maximum score estimation; generated regressor; preference parameters; M-estimation; cube root asymptotics (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 (search for similar items in EconPapers)
Date: 2013-04-23
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-upt
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Working Paper: Maximum score estimation of preference parameters for a binary choice model under uncertainty (2013)
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