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Edgeworth Expansion for the Whittle Maximum Likelihood Estimator of Linear Regression Processes with Long Memory Residuals

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  • Mosisa Aga

Abstract

We establish an Edgeworth expansion for the distribution of the Whittle maximum likelihood estimator of the parameter of a time series generated by a linear regression model with Gaussian, stationary, and long-memory residuals. This is done by imposing an extra condition on coefficients of the regression model in addition to the standard conditions imposed on the the spectral density function and the parameter values and making use of the results of Andrews et al. (2005), who provided an Edgeworth expansion for the residual component.

Suggested Citation

  • Mosisa Aga, 2021. "Edgeworth Expansion for the Whittle Maximum Likelihood Estimator of Linear Regression Processes with Long Memory Residuals," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 10(4), pages 119-119, July.
    • Handle: RePEc:ibn:ijspjl:v:10:y:2021:i:4:p:119
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    References listed on IDEAS

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    1. Andrews, Donald W.K. & Lieberman, Offer, 2005. "Valid Edgeworth Expansions For The Whittle Maximum Likelihood Estimator For Stationary Long-Memory Gaussian Time Series," Econometric Theory, Cambridge University Press, vol. 21(4), pages 710-734, August.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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