Asymptotic Distribution of the Cointegrating Vector Estimator in Error Correction Models with Conditional Heteroskedasticity
Asymptotic Distribution of the Cointegrating Vector Estimator in Error Correction Models with Conditional Heteroskedasticity
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条件异方差误差修正模型中协整向量估计量的渐近分布
DOI：
10.1016/j.jeconom.2006.03.008
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Byeongseon Seo
中科院分区：
文献类型：
--
作者：
Byeongseon Seo
本文研究了条件异方差误差修正模型中协整向量估计的渐近分布。协整向量的最大似然估计（MLE），估计协整向量和多元Gestival过程联合，提供的渐近性质。协整向量的极大似然估计服从混合正态分布，其渐近分布依赖于标准化新息的条件异方差性和峰度。降秩回归（RRR）估计和基于回归的协整向量估计不考虑条件异方差，因此MLE的效率增益出现的大小的条件异方差的增加。仿真结果表明，基于极大似然估计的t-统计量的相对功效随着Gestion效应的增加而显著提高。
This paper explores the asymptotic distribution of the cointegrating vector estimator in error correction models with conditionally heteroskedastic errors. Asymptotic properties of the maximum likelihood estimator (MLE) of the cointegrating vector, which estimates the cointegrating vector and the multivariate GARCH process jointly, are provided. The MLE of the cointegrating vector follows mixture normal, and its asymptotic distribution depends on the conditional heteroskedasticity and the kurtosis of standardized innovations. The reduced rank regression (RRR) estimator and the regression-based cointegrating vector estimators do not consider conditional heteroskedasticity, and thus the efficiency gain of the MLE emerges as the magnitude of conditional heteroskedasticity increases. The simulation results indicate that the relative power of the t-statistics based on the MLE improves significantly as the GARCH effect increases.