Consistency of averaged impulse response estimators in vector autoregressive models

IF 1.2 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Jan Lohmeyer, Franz Palm, Jean-Pierre Urbain
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引用次数: 0

Abstract

We show root-T consistency of the smoothed AIC and smoothed BIC model averaging estimators (sAIC, sBIC) of impulse response coefficients in stationary vector autoregressive models of finite lag order. We also show that there is not one unique way to define the sAIC and sBIC estimators, but that instead there is a whole class of each of these defined by a weight scaling factor that allows the averaging estimator to become more similar to either its model selection counterpart or the equal weights averaging estimator. We also show asymptotic validity of a bootstrap method for estimating the averaging estimators' distributions. Simulations illustrate the benefits of using sAIC instead of AIC estimators.

向量自回归模型中平均脉冲响应估计器的一致性
我们证明了有限滞后阶静止向量自回归模型中脉冲响应系数的平滑 AIC 和平滑 BIC 模型平均估计器(sAIC、sBIC)的根 T 一致性。我们还证明,定义 sAIC 和 sBIC 估计数的方法并不唯一,而是存在着一整类由权重缩放因子定义的估计器,这些权重缩放因子可使平均估计器变得与其模型选择对应物或等权重平均估计器更加相似。我们还展示了用于估计平均估计器分布的自举法的渐近有效性。模拟说明了使用 sAIC 代替 AIC 估计器的好处。
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来源期刊
Journal of Time Series Analysis
Journal of Time Series Analysis 数学-数学跨学科应用
CiteScore
2.00
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.
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