Semiparametric estimation of a heteroskedastic sample selection model

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This paper considers estimation of a sample selection model subject to conditional heteroskedasticity in both the selection and outcome equations. The form of heteroskedasticity allowed for in each equation is multiplicative, and each of the two scale functions is left unspecified. A three-step estimator for the parameters of interest in the outcome equation is proposed. The first two stages involve nonparametric estimation of the "propensity score" and the conditional interquartile range of the outcome equation, respectively. The third stage reweights the data so that the conditional expectation of the reweighted dependent variable is of a partially linear form, and the parameters of interest are estimated by an approach analogous to that adopted in Ahn and Powell (1993, Journal of Econometrics 58, 3-29). Under standard regularity conditions the proposed estimator is shown to be √n-consistent and asymptotically normal, and the form of its limiting covariance matrix is derived.






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Chen, S, and S Khan (2003). Semiparametric estimation of a heteroskedastic sample selection model. Econometric Theory, 19(6). pp. 1040–1064. 10.1017/S0266466603196077 Retrieved from

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Shakeeb Khan

Professor of Economics

Professor Khan is on leave at Boston College for the 2016-17 academic year.

Professor Khan specializes in the fields of mathematical economics, statistics, and applied econometrics. His studies have explored a variety of subjects from covariate dependent censoring and non-stationary panel data, to causal effects of education on wage inequality and the variables affecting infant mortality rates in Brazil. He was awarded funding by National Science Foundation grants for his projects entitled, “Estimation of Binary Choice and Nonparametric Censored Regression Models” and “Estimation of Cross-Sectional and Panel Data Duration Models with General Forms of Censoring.” He has published numerous papers in leading academic journals, including such writings as, “Heteroskedastic Transformation Models with Covariate Dependent Censoring” with E. Tamer and Y. Shin; “The Identification Power of Equilibrium in Simple Games;” “Partial Rank Estimation of Duration Models with General Forms of Censoring” with E. Tamer; and more. He is currently collaborating with D. Nekipelov and J.L. Powell on the project, “Optimal Point and Set Inference in Competing Risk Models;” with A. Lewbel on, “Identification and Estimation of Stochastic Frontier Models;” and with E. Tamer on, “Conditional Moment Inequalities in Roy Models with Cross-Section and Panel Data.”

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