Modified Information Criteria and Selection of Long Memory Time Series Models

Richard Baillie, George Kapetanios, Fotis Papailias

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The problem of model selection of a univariate long memory time series is investigated once a semi parametric estimator for the long memory parameter has been used. Standard information criteria are not consistent in this case. A Modified Information Criterion (MIC) that overcomes these difficulties is introduced and proofs that show its asymptotic validity are provided. The results are general and cover a wide range of short memory processes. Simulation evidence compares the new and existing methodologies and empirical applications in monthly inflation and daily realized volatility are presented.
Original languageEnglish
Pages (from-to)116-131
JournalComputational Statistics & Data Analysis
Volume76
Early online date03 May 2013
DOIs
Publication statusPublished - Aug 2014

Keywords

  • Long memory
  • ARFIMA models
  • Modified Information Criteria

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