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GLS Bias Correction for Low Order ARMA models


  • Patrick Richard

    () (GREDI, Département d'économique, Université de Sherbrooke)


We study the problems of bias correction in the estimation of low order ARMA(p, q) time series models. We introduce a new method to estimate the bias of the parameters of ARMA(p, q) process based on the analytical form of the GLS transformation matrix of Galbraith and Zinde-Walsh (1992). We show that the resulting bias corrected estimator is consistent and asymptotically normal. We also argue that, in the case of an MA(q) model, our method may be considered as an iteration of the analytical indirect inference technique of Galbraith and Zinde-Walsh (1994). The potential of our method is illustrated through a series of Monte Carlo experiments.

Suggested Citation

  • Patrick Richard, 2007. "GLS Bias Correction for Low Order ARMA models," Cahiers de recherche 07-19, Departement d'Economique de l'École de gestion à l'Université de Sherbrooke.
  • Handle: RePEc:shr:wpaper:07-19

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    References listed on IDEAS

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    Cited by:

    1. Patrick Richard, 2009. "Improving the accuracy of the analytical indirect inference estimator for MA models," Economics Bulletin, AccessEcon, vol. 29(4), pages 2795-2802.

    More about this item


    ARMA; bias correction; GLS;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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