Nonparametric and Semiparametric Estimation of Additive Models with both Discrete and Continuous Variables under Dependence
AbstractThis paper is concerned with the estimation and inference of nonparametric and semiparametric additive models in the presence of discrete variables and dependent observations. Among the different estimation procedures, the method introduced by Linton and Nielsen, based in marginal integration, has became quite popular because both its computational simplicity and the fact that it allows an asymptotic distribution theory. Here, an asymptotic treatment of the marginal integration estimator under different mixtures of continuous-discrete variables is offered, and furthermore, in the semiparametric partially additive setting, an estimator for the parametric part that is consistent and asymptotically efficient is proposed. The estimator is based in minimizing the L2 distance between the additive nonparametric component and its correspondent linear direction. Finally, we present an application to show the feasibility of all methods introduced in the paper. --
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Bibliographic InfoPaper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2003,38.
Date of creation: 2003
Date of revision:
Additive Models; Dimension reduction techniques; semiparametric models; strong mixing conditions; marginal integration;
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- Donald W.K. Andrews & Yoon-Jae Whang, 1989.
"Additive Interactive Regression Models: Circumvention of the Curse of Dimensionality,"
Cowles Foundation Discussion Papers
925, Cowles Foundation for Research in Economics, Yale University.
- Andrews, Donald W.K. & Whang, Yoon-Jae, 1990. "Additive Interactive Regression Models: Circumvention of the Curse of Dimensionality," Econometric Theory, Cambridge University Press, vol. 6(04), pages 466-479, December.
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