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A simplified GLS estimator for autoregressive moving-average models

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  • Askar Choudhury
  • Simon Power

Abstract

Koreisha and Pukkila (1990a) have recently proposed a fast and efficient GLS estimator for the univariate ARMA time series model which appears to be far more robust than maximum likelihood methods and of comparable accuracy. The one drawback to this new estimator is that it requires use of the Cholesky decomposition. The purpose of this paper is to suggest an alternative simplified GLS estimator, which can be implemented with just repeated applications of an OLS subroutine. A limited Monte Carlo study establishes that this new estimator is just as efficient as that of Koreisha and Pukkila.

Suggested Citation

  • Askar Choudhury & Simon Power, 1998. "A simplified GLS estimator for autoregressive moving-average models," Applied Economics Letters, Taylor & Francis Journals, vol. 5(4), pages 247-250.
    • Handle: RePEc:taf:apeclt:v:5:y:1998:i:4:p:247-250
    DOI: 10.1080/135048598354915
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    References listed on IDEAS

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    1. Park, Choon Y. & Heikes, Russell G., 1983. "A note on Balestra's (1980) approximate estimator for the first-order moving average process," Journal of Econometrics, Elsevier, vol. 21(3), pages 387-388, April.
    2. Choudhury, Askar H. & St. Louis, Robert D., 1990. "A note on Park and Heikes' (1983) modified approximate estimator for the first-order moving-average process," Journal of Econometrics, Elsevier, vol. 46(3), pages 399-406, December.
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    1. Choudhury, Askar H. & Power, Simon, 1995. "A new approximate GLS estimator for the linear regression model with ARMA(p, q) disturbances," Economics Letters, Elsevier, vol. 48(2), pages 119-127, May.

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