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Limiting distribution of the least squaresestimates in polynomial regression with longmemory noises

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  • Mohamed Boutahar

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

We give the limiting distribution of the least squares estimator in the polynomial regression model driven by some long memory processes. We prove that with an appropriate normalization, the estimation error converges, in distribution, to a random vector which components are a mixture of stochastic integrals. These integrals are with respect to a Lebesgue measure, and can be computed recursively where the seed is a random variable which depends on the assumptions made on the noise process. The limiting distribution can be Gaussian or non Gaussian.

Suggested Citation

  • Mohamed Boutahar, 2006. "Limiting distribution of the least squaresestimates in polynomial regression with longmemory noises," Working Papers halshs-00409571, HAL.
    • Handle: RePEc:hal:wpaper:halshs-00409571
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00409571
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    References listed on IDEAS

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