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Stochastic Regression Model with Dependent Disturbances

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  • Kokyo Choy
  • Masanobu Taniguchi

Abstract

In this paper, we consider the estimation of the coefficient of a stochastic regression model whose explanatory variables and disturbances are permitted to exhibit short‐memory or long‐memory dependence. Three estimators of the coefficient are proposed. A variety of their asymptotics are illuminated under various assumptions on the explanatory variables and the disturbances. Numerical studies of the theoretical results are given. They show some unexpected aspects of the asymptotics of the three estimators.

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  • Kokyo Choy & Masanobu Taniguchi, 2001. "Stochastic Regression Model with Dependent Disturbances," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(2), pages 175-196, March.
    • Handle: RePEc:bla:jtsera:v:22:y:2001:i:2:p:175-196
    DOI: 10.1111/1467-9892.00218
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    Cited by:

    1. George Kapetanios & Zacharias Psaradakis, 2016. "Semiparametric Sieve-Type Generalized Least Squares Inference," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 951-985, June.
    2. George Kapetanios & Zacharias Psaradakis, 2007. "Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence," Working Papers 587, Queen Mary University of London, School of Economics and Finance.
    3. George Kapetanios & Zacharias Psaradakis, 2007. "Semiparametric Sieve-Type GLS Inference in Regressions with Long-Range Dependence," Working Papers 587, Queen Mary University of London, School of Economics and Finance.

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