| dc.contributor | 財政系 | en_US |
| dc.creator (作者) | 蔡文禎 | zh_TW |
| dc.creator (作者) | Tsay, Wen-Jen | en_US |
| dc.date (日期) | 2010.07 | en_US |
| dc.date.accessioned | 17-Apr-2014 16:26:15 (UTC+8) | - |
| dc.date.available | 17-Apr-2014 16:26:15 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Apr-2014 16:26:15 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/65470 | - |
| dc.description.abstract (摘要) | 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. | en_US |
| dc.format.extent | 138 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journal of Statistical Computation and Simulation, 80(7), 729-745 | en_US |
| dc.subject (關鍵詞) | Durbin-Levinson algorithm; Long memory; Maximum likelihood estimation; Multivariate time series | en_US |
| dc.title (題名) | Maximum Likelihood Estimation of Stationary Multivariate ARFIMA Processes. | en_US |
| dc.type (資料類型) | article | en |
