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Maximum likelihood estimation in vector long memory processes via EM algorithm

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  • Pai, Jeffrey
  • Ravishanker, Nalini

Abstract

We present an approach for exact maximum likelihood estimation of parameters from univariate and multivariate autoregressive fractionally integrated moving average models with Gaussian errors using the Expectation Maximization (EM) algorithm. The method takes advantage of the relation between the VARFIMA(0,d,0) process and the corresponding VARFIMA(p,d,q) process in the computation of the likelihood.

Suggested Citation

  • Pai, Jeffrey & Ravishanker, Nalini, 2009. "Maximum likelihood estimation in vector long memory processes via EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4133-4142, October.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4133-4142
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    References listed on IDEAS

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    1. Wen-Jen Tsay, 2007. "Maximum Likelihood Estimation of Stationary Multivariate ARFIMA Processes," IEAS Working Paper : academic research 07-A011, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    2. Hosoya, Yuzo, 1996. "The quasi-likelihood approach to statistical inference on multiple time-series with long-range dependence," Journal of Econometrics, Elsevier, vol. 73(1), pages 217-236, July.
    3. Martin, Vance L. & Wilkins, Nigel P., 1999. "Indirect estimation of ARFIMA and VARFIMA models," Journal of Econometrics, Elsevier, vol. 93(1), pages 149-175, November.
    4. Heyde, C. C. & Gay, R., 1993. "Smoothed periodogram asymptotics and estimation for processes and fields with possible long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 45(1), pages 169-182, March.
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    Cited by:

    1. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    2. Stefanos Kechagias & Vladas Pipiras, 2020. "Modeling bivariate long‐range dependence with general phase," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(2), pages 268-292, March.
    3. Pai, Jeffrey & Ravishanker, Nalini, 2015. "Fast approximate likelihood evaluation for stable VARFIMA processes," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 160-168.

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