Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models

Donald Andrews ()

No 874R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: This paper establishes the asymptotic normality of series estimators for nonparametric regression models. Gallant's Fourier flexible form estimators, trigonometric series estimators, and polynomial series estimators are prime examples of the estimators covered by the results. The results apply to a wide variety of estimands in the regression model under consideration, including derivatives and integrals of the regression function. The errors in the model may be homoskedastic or heteroskeclastic. The paper also considers series estimators for additive interactive regression (AIR), seimparametric regression, and semiparametric index regression models and shows them to be consistent and asymptotically normal. All of the consistency and asymptotic normality results in the paper follow from one set of general results for series estimators.

Keywords: Asymptotic normality; nonparametric regression; polynomial series; semiparametric regression; series estimators (search for similar items in EconPapers)
Pages: 70 pages
Date: 1988, Revised 1989-05
Note: CFP 776.
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Citations: View citations in EconPapers (1)

Published in Econometrica (March 1991), 59(2): 307-345

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Journal Article: Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression Models (1991)
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