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Endogeneity in Quantile Regression Models: A Control Function Approach Author info | Abstract | Publisher info | Download info | Related research | Statistics Sokbae Lee
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This paper considers a linear triangular simultaneous equations model with conditional quantile restrictions. The paper adjusts for endogeneity by adopting a control function approach and presents a simple two-step estimator that exploits the partially linear structure of the model. The first step consists of estimation of the residuals of the reduced-form equation for the endogenous explanatory variable. The second step is series estimation of the primary equation with the reduced-form residual included nonparametrically as an additional explanatory variable. This paper imposes no functional form restrictions on the stochastic relationship between the reduced-form residual and the disturbance term in the primary equation conditional on observable explanatory variables. The paper presents regularity conditions for consistency and asymptotic normality of the two-step estimator. In addition, the paper provides some discussions on related estimation methods in the literature and on possible extensions and limitations of the estimation approach. Finally, the numerical performance and usefulness of the estimator are illustrated by the results of Monte Carlo experiments and two empirical examples, demand for fish and returns to schooling
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Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number
521.
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Date of creation: 11 Aug 2004Date of revision:
Handle: RePEc:ecm:nasm04:521Contact details of provider: Phone: 1 212 998 3820 Fax: 1 212 995 4487 Email: Web page: http://www.econometricsociety.org/pastmeetings.asp More information through EDIRC
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Keywords: Endogeneity ; partially linear regression ; quantile regression ; series estimation. ; Other versions of this item:
Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
This paper has been announced in the following NEP Reports :
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