A VAR approach to forecasting multivariate long memory processes subject to structural breaks

Cindy S H WANG*, Shui Ki WAN

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

Abstract

This chapter extends the univariate forecasting method proposed by Wang, Luc, and Hsiao (2013) to forecast the multivariate long memory model subject to structural breaks. The approach does not need to estimate the parameters of this multivariate system nor need to detect the structural breaks. The only procedure is to employ a VAR(k) model to approximate the multivariate long memory model subject to structural breaks. Therefore, this approach reduces the computational burden substantially and also avoids estimation of the parameters of the multivariate long memory model, which can lead to poor forecasting performance. Moreover, when there are multiple breaks, when the breaks occur close to the end of the sample or when the breaks occur at different locations for the time series in the system, our VAR approximation approach solves the issue of spurious breaks in finite samples, even though the exact orders of the multivariate long memory process are unknown. Insights from our theoretical analysis are confirmed by a set of Monte Carlo experiments, through which we demonstrate that our approach provides a substantial improvement over existing multivariate prediction methods. Finally, an empirical application to the multivariate realized volatility illustrates the usefulness of our forecasting procedure.
Original languageEnglish
Title of host publicationEssays in Honor of Cheng Hsiao
EditorsTong Li, M. Hashem Pesaran, Dek Terrell
PublisherEmerald Publishing
Pages105-141
Number of pages37
ISBN (Electronic)9781789739572
ISBN (Print)9781789739589
DOIs
Publication statusPublished - 15 Apr 2020

Publication series

NameAdvances in Econometrics
Volume41
ISSN (Print)0731-9053

User-Defined Keywords

  • AR approximation
  • VAR approximation
  • Multivariate long memory processes
  • Structural breaks
  • ARFIMA model
  • Common break
  • C22
  • C53

Fingerprint

Dive into the research topics of 'A VAR approach to forecasting multivariate long memory processes subject to structural breaks'. Together they form a unique fingerprint.

Cite this