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Estimating weak periodic vector autoregressive time series

Author

Listed:
  • Yacouba Boubacar Maïnassara

    (Université Bourgogne Franche-Comté)

  • Eugen Ursu

    (Université de Bordeaux
    West University of Timisoara)

Abstract

This article develops the asymptotic distribution of the least squares estimator of the model parameters in periodic vector autoregressive time series models (hereafter PVAR) with uncorrelated but dependent innovations. When the innovations are dependent, this asymptotic distributions can be quite different from that of PVAR models with independent and identically distributed (iid for short) innovations developed (Ursu and Duchesne in J Time Ser Anal 30:70–96, 2009). Modified versions of the Wald tests are proposed for testing linear restrictions on the parameters. These asymptotic results are illustrated by Monte Carlo experiments. An application to a bivariate real financial data is also proposed.

Suggested Citation

  • Yacouba Boubacar Maïnassara & Eugen Ursu, 2023. "Estimating weak periodic vector autoregressive time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(3), pages 958-997, September.
    • Handle: RePEc:spr:testjl:v:32:y:2023:i:3:d:10.1007_s11749-023-00859-w
    DOI: 10.1007/s11749-023-00859-w
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    References listed on IDEAS

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    6. Eben Lazarus & Daniel J. Lewis & James H. Stock & Mark W. Watson, 2018. "HAR Inference: Recommendations for Practice Rejoinder," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 574-575, October.
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