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Two-step generalised empirical likelihood inference for semiparametric models

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  • Bravo, Francesco

Abstract

This paper shows how the generalised empirical likelihood method can be used to obtain valid asymptotic inference for the finite dimensional component of semiparametric models defined by a set of moment conditions. The results of the paper are illustrated using three well-known semiparametric regression models: partially linear single index, linear transformation with random censoring, and quantile regression with random censoring. Monte Carlo simulations suggest that some of the proposed test statistics have competitive finite sample properties. The results of the paper are applied to test for functional misspecification in a hedonic price model of a housing market.

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  • Bravo, Francesco, 2009. "Two-step generalised empirical likelihood inference for semiparametric models," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1412-1431, August.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:7:p:1412-1431
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    Cited by:

    1. Francesco Bravo, 2014. "Varying coefficients partially linear models with randomly censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 383-412, April.
    2. Zaichao Du & Juan Carlos Escanciano, 2015. "A Nonparametric Distribution-Free Test for Serial Independence of Errors," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1011-1034, December.
    3. Bravo, Francesco & Chu, Ba M. & Jacho-Chávez, David T., 2017. "Generalized empirical likelihood M testing for semiparametric models with time series data," Econometrics and Statistics, Elsevier, vol. 4(C), pages 18-30.

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