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.
Authors
Research Fellow Columbia University
Sokbae is an IFS Research Fellow and a Professor at Columbia University, with an interest in Econometrics, Applied Microeconomics and Statistics.
Academia Sinica
Myung Jae Sung
Working Paper details
- DOI
- 10.1920/wp.cem.2013.1413
- Publisher
- Cemmap
Suggested citation
L, Chen and S, Lee and M, Sung. (2013). Maximum score estimation of preference parameters for a binary choice model under uncertainty. London: Cemmap. Available at: https://ifs.org.uk/publications/maximum-score-estimation-preference-parameters-binary-choice-model-under-uncertainty (accessed: 20 April 2024).
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