Asymptotic theory for partly linear models

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.

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Year of publication:	1994-07-01
Authors:	Gao, Jiti
Institutions:	Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München
Subject:	Asymptotic normality \| linear process \| partly linear model \| strong consistency

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Extent:	application/pdf
Series:	MPRA Paper.
Type of publication:	Book / Working Paper
Notes:	Published in Communications in Statistics: Theory and Methods 8.24(1995): pp. 1985-2009
Classification:	C14 - Semiparametric and Nonparametric Methods ; C22 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011112439