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.
Published Version (Please cite this version)10.1017/S0266466603196077
Publication InfoChen, S; & Khan, S (2003). Semiparametric estimation of a heteroskedastic sample selection model. Econometric Theory, 19(6). pp. 1040-1064. 10.1017/S0266466603196077. Retrieved from https://hdl.handle.net/10161/2541.
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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 ent