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Nonparametric Identification and Estimation of Panel Quantile Models with Sample Selection

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  • Sungwon Lee

    (Department of Economics, Sogang University, Seoul)

Abstract

This paper develops nonparametric panel quantile regression models with sample selection. The class of models allows the unobserved heterogeneity to be correlated with time-varying regressors in a time-invariant manner. I adopt the correlated random effects approach proposed by Mundlak (1978) and Chamberlain (1980), and the control function approach to correct the sample selection bias. The class of models is general and flexible enough to incorporate many empirical issues, such as endogeneity of regressors and censoring. Identification of the model requires that T≥3, where T is the number of time periods, and that there is an excluded variable that affects the selection probability. Based on the identification result, this paper proposes sieve two-step estimation to estimate the model parameters. This paper also establishes the asymptotic theory for the sieve two-step estimators, including consistency, convergence rates, and asymptotic normality of functionals.

Suggested Citation

  • Sungwon Lee, 2020. "Nonparametric Identification and Estimation of Panel Quantile Models with Sample Selection," Working Papers 2012, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    • Handle: RePEc:sgo:wpaper:2012
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    References listed on IDEAS

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    9. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
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    11. Joseph G. Altonji & Rosa L. Matzkin, 2005. "Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors," Econometrica, Econometric Society, vol. 73(4), pages 1053-1102, July.
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    More about this item

    Keywords

    Sample selection; panel data; quantile regression; nonseparable models; correlated random effects; control function approach; nonparametric identification; sieve two-step estimation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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