估计员
自回归模型
数学
渐近分布
单变量
系列(地层学)
区间(图论)
自回归滑动平均模型
一致性(知识库)
自回归积分移动平均
星型
应用数学
统计
移动平均线
时间序列
多元统计
离散数学
组合数学
古生物学
生物
作者
Han Ai,Yun Sik Hong,Shouyang Wang,Yun Xin
出处
期刊:Advances in econometrics
日期:2016-06-23
卷期号:: 417-460
被引量:12
标识
DOI:10.1108/s0731-905320160000036021
摘要
Modelling and forecasting interval-valued time series (ITS) have received increasing attention in statistics and econometrics. An interval-valued observation contains more information than a point-valued observation in the same time period. The previous literature has mainly considered modelling and forecasting a univariate ITS. However, few works attempt to model a vector process of ITS. In this paper, we propose an interval-valued vector autoregressive moving average (IVARMA) model to capture the cross-dependence dynamics within an ITS vector system. A minimum-distance estimation method is developed to estimate the parameters of an IVARMA model, and consistency, asymptotic normality and asymptotic efficiency of the proposed estimator are established. A two-stage minimum-distance estimator is shown to be asymptotically most efficient among the class of minimum-distance estimators. Simulation studies show that the two-stage estimator indeed outperforms other minimum-distance estimators for various data-generating processes considered.
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