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. 1985-2009.
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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|
|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 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
|Depositing User:||Jiti Gao|
|Date Deposited:||04. Aug 2012 02:33|
|Last Modified:||15. Feb 2013 04:53|
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