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  • 标题:Unit Root Model Selection
  • 本地全文:下载
  • 作者:Peter C. B. Phillips
  • 期刊名称:JOURNAL OF THE JAPAN STATISTICAL SOCIETY
  • 印刷版ISSN:1882-2754
  • 电子版ISSN:1348-6365
  • 出版年度:2008
  • 卷号:38
  • 期号:1
  • 页码:65-74
  • DOI:10.14490/jjss.38.65
  • 出版社:JAPAN STATISTICAL SOCIETY
  • 摘要:Some limit properties for information based model selection criteria are given in the context of unit root evaluation and various assumptions about initial conditions. Allowing for a nonparametric short memory component, standard information criteria are shown to be weakly consistent for a unit root provided the penalty coefficient Cn→∞ and Cn/n→ 0 as n→∞ . Strong consistency holds when Cn/ (log log n )3 →∞ under conventional assumptions on initial conditions and under a slightly stronger condition when initial conditions are infinitely distant in the unit root model. The limit distribution of the AIC criterion is obtained.
  • 关键词:AIC;consistency;model selection;nonparametric;unit root
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