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Some notes on statistic robustness of nonparametric bivariate probit model in a finite sample

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  • Takaaki Aoki

Abstract

This article describes qualitatively some interesting statistic aspects of the nonparametric bivariate Probit model, which was examined in Aoki (2005) as a nonparametrically modified version of the estimator to test asymmetric information, originally proposed in Chiappori and Salanie (2000). My computation results and analysis show that even in a finite sample case the nonparametric version is very robust to the variable bandwidth, which is relatively smaller than the optimal bandwidth policy. This statistic characteristics enables the proposed nonparametric estimator to be put widely and conveniently into practical use, without applied researcher's necessity to pay too much attention to the precise value of optimal bandwidth.

Suggested Citation

  • Takaaki Aoki, 2009. "Some notes on statistic robustness of nonparametric bivariate probit model in a finite sample," Applied Economics Letters, Taylor & Francis Journals, vol. 16(5), pages 443-447.
    • Handle: RePEc:taf:apeclt:v:16:y:2009:i:5:p:443-447
    DOI: 10.1080/13504850601032115
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    References listed on IDEAS

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    1. Takaaki Aoki, 2005. "Proposed modified probit model incorporating non-parametric density estimation: how to measure asymmetric information in the health insurance market?," Applied Economics Letters, Taylor & Francis Journals, vol. 12(6), pages 347-350.
    2. Pierre-Andre Chiappori & Bernard Salanie, 2000. "Testing for Asymmetric Information in Insurance Markets," Journal of Political Economy, University of Chicago Press, vol. 108(1), pages 56-78, February.
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