Robust quasi-likelihood estimation for the negative binomial integer-valued GARCH(1,1) model with an application to transaction counts
Robust quasi-likelihood estimation for the negative binomial integer-valued GARCH(1,1) model with an application to transaction counts
复制标题
负二项式整数值 GARCH(1,1) 模型的稳健拟似然估计及其在交易计数中的应用
DOI：
10.1016/j.jspi.2019.03.010
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发表时间：
2019-12-01
影响因子：
0.9
通讯作者：
Zhu, Fukang
中科院分区：
文献类型：
--
作者：
Xiong, Lanyu;Zhu, Fukang
对于计数时间序列分析，Poisson整数值广义自回归条件异方差模型是非常流行的，但通常不适用于存在潜在极值观测的情况。极大似然估计是参数估计的一种常用方法，但极大似然估计受异常值的影响较大。本文有三个主要目的。首先，我们将负二项模型应用于计数时间序列分析，并考虑此模型的极大似然估计。其次，我们将广义线性模型中提出的Mallows拟似然方法推广到我们的情形。此外，在一定的正则性条件下，证明了所得稳健估计的相合性和渐近正态性。第三，通过仿真研究了这些鲁棒估计器在瞬态漂移和加性离群值存在下的性能。我们将稳健估计应用于两个股票市场数据集，并通过样本内和样本外预测来评估其预测性能。(C)2019爱思唯尔B.V.保留所有权利。
For count time series analysis, the Poisson integer-valued generalized autoregressive conditional heteroscedastic model is very popular but is not usually suitable in the existence of potential extreme observations. Maximum likelihood estimator is commonly used to estimate parameters, but it is highly affected by the outliers. This paper has three main aims. First, we apply the negative binomial model in our study for count time series analysis and consider the maximum likelihood estimation of this model. Second, we extend the Mallows' quasi-likelihood method proposed in the generalized linear models to our situation. Besides, we establish the consistency and asymptotic normality for the resulting robust estimators under some regularity conditions. Third, the performances of these robust estimators in the presence of transient shifts and additive outliers are investigated via simulations. We apply the robust estimator to two stock-market data sets and their prediction performances are assessed by in-sample and out-of-sample predictions. (C) 2019 Elsevier B.V. All rights reserved.