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Maximum likelihood estimation in vector long memory processes via EM algorithm

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  • Pai, Jeffrey
  • Ravishanker, Nalini
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    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.

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    File URL: http://www.sciencedirect.com/science/article/B6V8V-4W99VTH-3/2/4c0c946ef3dc5237ec80a6dea740ac57
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 53 (2009)
    Issue (Month): 12 (October)
    Pages: 4133-4142

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    Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:4133-4142

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    Web page: http://www.elsevier.com/locate/csda

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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. 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.
    3. Martin, V.L. & Wilkins, N.P., 1997. "Indirect Estimation of Arfima and Varfima Models," Department of Economics - Working Papers Series 547, The University of Melbourne.
    4. 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.
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