Jackknife estimation of stationary autoregressive models
Jackknife estimation of stationary autoregressive models
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DOI：
10.1016/j.jeconom.2012.09.003
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发表时间：
2013-01-01
影响因子：
6.3
通讯作者：
Chambers, Marcus J.
中科院分区：
文献类型：
--
作者：
Chambers, Marcus J.
本文研究了平稳自回归模型的折刀估计方法的性质。对于p阶平稳自回归，当子样本数量固定或随样本量以适当的速率增加时，给出了基于非重叠子样本的叠刀估计量的渐近性质。详细研究了各种叠刀估计器和替代估计器的有限样本性质的结果，发现叠刀可以在自回归模型中提供大量的偏差减少。这一发现对于偏离正态、ARCH效应和错误规范是稳健的。还研究了中位数无偏性和均方误差特性，并与其他方法进行了比较，以及基于jackknife的置信区间的覆盖率。(C) 2012 Elsevier B.V.版权所有
This paper explores the properties of jackknife methods of estimation in stationary autoregressive models. Some general results concerning the correct weights for bias reduction under various sampling schemes are provided and the asymptotic properties of a jackknife estimator based on non-overlapping sub-samples are derived for the case of a stationary autoregression of order p when the number of sub-samples is either fixed or increases with the sample size at an appropriate rate. The results of a detailed investigation into the finite sample properties of various jackknife and alternative estimators are reported and it is found that the jackknife can deliver substantial reductions in bias in autoregressive models. This finding is robust to departures from normality, ARCH effects and misspecification. The median-unbiasedness and mean squared error properties are also investigated and compared with alternative methods as are the coverage rates of jackknife-based confidence intervals. (C) 2012 Elsevier B.V. All rights reserved.