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分类依据与纠错

AI 根据所提供的论文材料归类:摘要研究时间域的分数单整检验、长记忆及短期动态处理,直接属于时间序列方法。

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关注这篇论文的作者
Matei Demetrescu
Vladimir Kuzin
Uwe Hassler

时域中的长记忆检验

研究问题
如何在单变量时间序列中检验单整性质对分数备择,并允许一般线性短记忆成分和可能存在的条件异方差创新?
方法与思路
作者提出完全基于回归的滞后增广LM检验,使滞后数随样本量增长,并用White标准误处理鞅差创新的条件异方差。
材料陈述的贡献
摘要称在上述扩展下统计量仍保持渐近正态,并通过蒙特卡洛考察有限样本可用性及多种模型选择策略。
阅读关注点
应核对被检验的单整阶数、滞后数增长条件、线性过程和鞅差假设,以及White标准误与模型选择的具体实现。
阅读边界
仅依据所给出版社摘要;滞后增长速率、完整正则条件与模拟数值未说明,不能将异方差处理推广到任意依赖创新。

原文摘要

An integration test against fractional alternatives is suggested for univariate time series. The new test is a completely regression-based, lag augmented version of the Lagrange multiplier (LM) test by Robinson (1991, Journal of Econometrics 47, 67–84). Our main contributions, however, are the following. First, we let the short memory component follow a general linear process. Second, the innovations driving this process are martingale differences with eventual conditional heteroskedasticity that is accounted for by means of White's standard errors. Third, we assume the number of lags to grow with the sample size, thus approximating the general linear process. Under these assumptions, limiting normality of the test statistic is retained. The usefulness of the asymptotic results for finite samples is established in Monte Carlo experiments. In particular, several strategies of model selection are studied.An earlier version of this paper was presented at the URCT Conference in Faro, Portugal, 2005, and at the Econometrics Seminar of the University of Zürich. We are in particular grateful to Peter Robinson and Michael Wolf for helpful comments. Moreover, we thank two anonymous referees for reports that greatly helped to improve the paper.

笔记与分组
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