Nonparametric Identification and Estimation of Panel Quantile Models with Sample Selection

Sungwon Lee ()
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Sungwon Lee: Department of Economics, Sogang University, Seoul

No 2012, Working Papers from Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy)

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.

Keywords: Sample selection; panel data; quantile regression; nonseparable models; correlated random effects; control function approach; nonparametric identification; sieve two-step estimation (search for similar items in EconPapers)
JEL-codes: C14 C21 C23 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2020
New Economics Papers: this item is included in nep-ecm and nep-ore
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