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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/65470


    Title: Maximum Likelihood Estimation of Stationary Multivariate ARFIMA Processes.
    Authors: 蔡文禎
    Tsay, Wen-Jen
    Contributors: 財政系
    Keywords: Durbin-Levinson algorithm;Long memory;Maximum likelihood estimation;Multivariate time series
    Date: 2010.07
    Issue Date: 2014-04-17 16:26:15 (UTC+8)
    Abstract: This paper considers the maximum likelihood estimation (MLE) of a class of stationary and invertible vector autoregressive fractionally integrated moving-average (VARFIMA) processes considered in (26) of Luce no [1] or Model A of Lobato [2] where each component yi,t is a fractionally integrated process of order di, i = 1, . . . , r. Under the conditions outlined in Assumption 1 of this paper, the conditional likelihood function of this class of VARFIMA models can be efficiently and exactly calculated with a conditional likelihood Durbin-Levinson (CLDL) algorithm proposed herein. This CLDL algorithm is based on the multivariate Durbin-Levinson algorithm of Whittle [3] and the conditional likelihood principle of Box and Jenkins [4]. Furthermore, the conditions in the aforementioned Assumption 1 are general enough to include the model considered in Andersen et al. [5] for describing the behavior of realized volatility and the model studied in Haslett and Raftery [6] for spatial data as its special cases. As the computational cost of implementing the CLDL algorithm is much lower than that of using the algorithms proposed in Sowell [7], we are thus able to conduct a Monte Carlo experiment to investigate the finite sample performance of the CLDL algorithm for the 3-dimensional VARFIMA processes with the sample size of 400. The simulation results are very satisfactory and reveal the great potentials of using the CLDL method for empirical applications.
    Relation: Journal of Statistical Computation and Simulation, 80(7), 729-745
    Data Type: article
    Appears in Collections:[Department of Public Finance] Periodical Articles

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