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Nonparametric Likelihood: Efficiency And Robustness

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  • YUICHI KITAMURA

Abstract

Nonparametric likelihood is a natural generalization of parametric likelihood and it offers effective methods for analysing economic models with nonparametric components. This is of great interest, since econometric theory rarely suggests a parametric form of the probability law of data. Being a nonparametric method, nonparametric likelihood is robust to misspecification. At the same time, it often achieves good properties that are analogous to those of parametric likelihood. This paper explores various applications of nonparametric likelihood, with some emphasis on the analysis of biased samples and data combination problems.

Suggested Citation

  • Yuichi Kitamura, 2007. "Nonparametric Likelihood: Efficiency And Robustness," The Japanese Economic Review, Japanese Economic Association, vol. 58(1), pages 26-46, March.
    • Handle: RePEc:bla:jecrev:v:58:y:2007:i:1:p:26-46
    DOI: 10.1111/j.1468-5876.2007.00416.x
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    References listed on IDEAS

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    1. Smith, Richard J., 2011. "Gel Criteria For Moment Condition Models," Econometric Theory, Cambridge University Press, vol. 27(6), pages 1192-1235, December.
    2. Tripathi, Gautam, 2011. "Moment-Based Inference With Stratified Data," Econometric Theory, Cambridge University Press, vol. 27(1), pages 47-73, February.
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    Cited by:

    1. Grendar, Marian & Judge, George G., 2010. "Maximum likelihood with estimating equations," CUDARE Working Papers 56691, University of California, Berkeley, Department of Agricultural and Resource Economics.
    2. Grendar, Marian & Judge, George G, 2009. "Maximum Empirical Likelihood: Empty Set Problem," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt71v338mh, Department of Agricultural & Resource Economics, UC Berkeley.
    3. Grendar, Marian & Judge, George G., 2010. "Revised empirical likelihood," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt6gs579r0, Department of Agricultural & Resource Economics, UC Berkeley.

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