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

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  • Jing-Jing Zhang
  • Han-Ying Liang
  • Amei Amei

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

Suggested Citation

  • Jing-Jing Zhang & Han-Ying Liang & Amei Amei, 2014. "Asymptotic normality of estimators in heteroscedastic errors-in-variables model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(2), pages 165-195, April.
    • Handle: RePEc:spr:alstar:v:98:y:2014:i:2:p:165-195
    DOI: 10.1007/s10182-013-0224-y
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    5. Guo-Liang Fan & Han-Ying Liang & Jiang-Feng Wang & Hong-Xia Xu, 2010. "Asymptotic properties for LS estimators in EV regression model with dependent errors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(1), pages 89-103, March.
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