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Semiparametric transformation model with endogeneity: a control function approach

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  • Van Keilegom, Ingrid
  • Vanhems, Anne
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    Abstract

    We consider a semiparametric transformation model, in which the regression function has an additive nonparametric structure and the transformation of the response is assumed to belong to some parametric family. We suppose that endogeneity is present in the explanatory variables. Using a control function approach, we show that the pro- posed model is identified under suitable assumptions, and propose a profile likelihood estimation method for the transformation. The proposed estimator is shown to be asymptotically normal under certain regularity conditions. A small simulation study shows that the estimator behaves well in practice.

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    Bibliographic Info

    Paper provided by Toulouse School of Economics (TSE) in its series TSE Working Papers with number 11-243.

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    Date of creation: 13 May 2011
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    Handle: RePEc:tse:wpaper:24640

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    2. David Jacho-Chávez & Arthur Lewbel & Oliver Linton, 2006. "Identification and nonparametric estimation of a transformed additively separable model," LSE Research Online Documents on Economics 4416, London School of Economics and Political Science, LSE Library.
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    13. Horowitz, Joel L, 1996. "Semiparametric Estimation of a Regression Model with an Unknown Transformation of the Dependent Variable," Econometrica, Econometric Society, vol. 64(1), pages 103-37, January.
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    17. Guido W. Imbens & Whitney K. Newey, 2002. "Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity," NBER Technical Working Papers 0285, National Bureau of Economic Research, Inc.
    18. Enno Mammen & Christoph Rothe & Melanie Schienle, 2010. "Nonparametric Regression with Nonparametrically Generated Covariates," SFB 649 Discussion Papers SFB649DP2010-059, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    19. Senay Sokullu, 2012. "Nonparametric Estimation of Semiparametric Transformation Models," Bristol Economics Discussion Papers 12/625, Department of Economics, University of Bristol, UK.
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