Asymptotic normality of estimators in heteroscedastic errors-in-variables model

Jing-Jing Zhang, Han-Ying Liang () and Amei Amei

AStA Advances in Statistical Analysis, 2014, vol. 98, issue 2, 165-195

Abstract: This article is concerned with the estimating problem of heteroscedastic partially linear errors-in-variables models. We derive the asymptotic normality for estimators of the slope parameter and the nonparametric component in the case of known error variance with stationary $$\alpha $$ α -mixing random errors. Also, when the error variance is unknown, the asymptotic normality for the estimators of the slope parameter and the nonparametric component as well as variance function is considered under independent assumptions. Finite sample behavior of the estimators is investigated via simulations too. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Partially linear errors-in-variables model; Heteroscedastic; Ordinary least-squares estimator; Weighted ordinary least-squares estimator; Asymptotic normality; $$\alpha $$ α -mixing; 62J12; 62E20 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:98:y:2014:i:2:p:165-195

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DOI: 10.1007/s10182-013-0224-y

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