Asymptotic theory for partly linear models

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.

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
• 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 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes

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