Gao, Jiti (1994): Asymptotic theory for partly linear models. Published in: Communications in Statistics: Theory and Methods , Vol. 24, No. 8 (7. April 1995): pp. 19852009.

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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.
Item Type:  MPRA Paper 

Original Title:  Asymptotic theory for partly linear models 
English Title:  Asymptotic Theory for Partly Linear Models 
Language:  English 
Keywords:  Asymptotic normality, linear process, partly linear model, strong consistency 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C14  Semiparametric and Nonparametric Methods: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes 
Item ID:  40452 
Depositing User:  Jiti Gao 
Date Deposited:  04. Aug 2012 02:33 
Last Modified:  19. Nov 2015 09:45 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/40452 