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A Generalized Least‐Squares Approach For Estimation Of Autoregressive Moving‐Average Models

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  • Sergio Koreisha
  • Tarmo Pukkila

Abstract

. In this paper we present a generalized least‐squares approach for estimating autoregressive moving‐average (ARMA) models. Simulation results based on different model structures with varying numbers of observations are used to contrast the performance of our procedure with that of maximum likelihood estimates. Existing software packages can be utilized to derive these estimates.

Suggested Citation

  • Sergio Koreisha & Tarmo Pukkila, 1990. "A Generalized Least‐Squares Approach For Estimation Of Autoregressive Moving‐Average Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 139-151, March.
    • Handle: RePEc:bla:jtsera:v:11:y:1990:i:2:p:139-151
    DOI: 10.1111/j.1467-9892.1990.tb00047.x
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    Cited by:

    1. Poloni, Federico & Sbrana, Giacomo, 2019. "Closed-form results for vector moving average models with a univariate estimation approach," Econometrics and Statistics, Elsevier, vol. 10(C), pages 27-52.
    2. Alfredo García Hiernaux & José Casals Carro & Miguel Jerez, 2005. "Fast estimation methods for time series models in state-space form," Documentos de Trabajo del ICAE 0504, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    3. D. S. Poskitt & M. O. Salau, 1995. "On The Relationship Between Generalized Least Squares And Gaussian Estimation Of Vector Arma Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(6), pages 617-645, November.
    4. Wanbo Lu & Rui Ke, 2019. "A generalized least squares estimation method for the autoregressive conditional duration model," Statistical Papers, Springer, vol. 60(1), pages 123-146, February.

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