Asymptotic theory for partly linear models

Jiti Gao

MPRA Paper from University Library of Munich, Germany

Abstract: This paper considers a partially linear model of the form y = x beta + g(t) + e, where beta is an unknown parameter vector, g(.) is an unknown function, and e is an error term. Based on a nonparametric estimate of g(.), the parameter beta is estimated by a semiparametric weighted least squares estimator. An asymptotic theory is established for the consistency of the estimators.

Keywords: Asymptotic normality; linear process; partly linear model; strong consistency (search for similar items in EconPapers)
JEL-codes: C14 C22 (search for similar items in EconPapers)
Date: 1994-07-01, Revised 1994-12-02
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Citations: View citations in EconPapers (8)

Published in Communications in Statistics: Theory and Methods 8.24(1995): pp. 1985-2009

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:40452

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