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Theory of Stochastic Processes, 2016, Volume 21(37), Issue 1, Pages 17–30 (Mi thsp117)  
Asymptotic normality of linear regression parameter estimator in the case of random regressors

A. V. Ivanov, I. V. Orlovsky

National technical university of Ukraine ”KPI”, Department of mathematical analysis and probability theory, Peremogi avenue 37, Kiev, Ukraine
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Abstract: Sufficient conditions of asymptotic normality of the least squares estimator of linear regression model parameter in the case of discrete time and weak or long-range dependent random regressors and noise are obtained in the paper.
Keywords: Asymptotic normality, least squares estimator, linear regression, random regressors, weak dependence, long-range dependence.
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Document Type: Article
MSC: Primary 62J02; Secondary 62J99
Language: English
Citation: A. V. Ivanov, I. V. Orlovsky, “Asymptotic normality of linear regression parameter estimator in the case of random regressors”, Theory Stoch. Process., 21(37):1 (2016), 17–30
Citation in format AMSBIB
\Bibitem{IvaOrl16}
\by A.~V.~Ivanov, I.~V.~Orlovsky
\paper Asymptotic normality of linear regression parameter estimator in the case of random regressors
\jour Theory Stoch. Process.
\yr 2016
\vol 21(37)
\issue 1
\pages 17--30
\mathnet{http://mi.mathnet.ru/thsp117}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=3571409}
\zmath{https://zbmath.org/?q=an:1363.62073}
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