Computationally efficient methods for two multivariate fractionally integrated models
Computationally efficient methods for two multivariate fractionally integrated models
复制标题
两个多元分数积分模型的计算有效方法
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Clifford M. Hurvich
中科院分区：
文献类型：
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作者：
Rebecca J. Sela;Clifford M. Hurvich
摘要：我们讨论了两个不同的多变量时间序列模型，它们扩展了单变量ARFIMA（自回归分数积分移动平均）模型。 我们讨论了这两个模型的不同含义，并描述了一个扩展到分数协整。我们描述的算法计算每个模型的协方差，计算二次型和近似的最大似然估计和模拟每个模型的行列式。我们比较的速度和准确性，每个算法与现有的方法单独。然后，我们衡量的最大似然估计和现有的方法在蒙特卡洛的性能。这些算法比现有的算法计算效率更高，同样准确，使其可行的多变量长记忆时间序列建模和模拟从这些模型。我们使用最大似然法来拟合美国商品和服务通胀数据的模型。
Abstract. We discuss two distinct multivariate time‐series models that extend the univariate ARFIMA (autoregressive fractionally integrated moving average) model. We discuss the different implications of the two models and describe an extension to fractional cointegration. We describe algorithms for computing the covariances of each model, for computing the quadratic form and approximating the determinant for maximum likelihood estimation and for simulating from each model. We compare the speed and accuracy of each algorithm with existing methods individually. Then, we measure the performance of the maximum likelihood estimator and of existing methods in a Monte Carlo. These algorithms are much more computationally efficient than the existing algorithms and are equally accurate, making it feasible to model multivariate long memory time series and to simulate from these models. We use maximum likelihood to fit models to data on goods and services inflation in the United States.