Determining the number of factors in a multivariate error correction–volatility factor model

Q. Li, J. Pan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In order to describe the comovements in both conditional mean and conditional variance of high dimensional nonstationary time series by dimension reduction, we introduce the conditional heteroscedasticity with factor structure to the error correction model. The new model is called the error correction volatility factor model. Some specification and estimation approaches are developed. In particular, the determination of the number of factors is discussed. Our setting is general in the sense that we impose neither i.i.d assumption on idiosyncratic components in the factor structure nor independence between factors and idiosyncratic errors. We illustrate the proposed approach with a Monte Carlo simulation and a real data example.
LanguageEnglish
Pages45-61
Number of pages17
JournalEconometrics Journal
Volume12
Issue number1
Early online date27 Nov 2008
DOIs
Publication statusPublished - Mar 2009

Fingerprint

Factors
Error correction
Non-stationary time series
Comovement
Monte Carlo simulation
Dimension reduction
Error correction model
Mean-variance
Conditional heteroscedasticity
Conditional variance

Keywords

  • dimension reduction
  • cointegration
  • error correction-volatility factor model
  • penalized goodness-of-fit criteria
  • model selection

Cite this

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Determining the number of factors in a multivariate error correction–volatility factor model. / Li, Q.; Pan, J.

In: Econometrics Journal, Vol. 12, No. 1, 03.2009, p. 45-61.

Research output: Contribution to journalArticle

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