Abstract
Language | English |
---|---|
Pages | 45-61 |
Number of pages | 17 |
Journal | Econometrics Journal |
Volume | 12 |
Issue number | 1 |
Early online date | 27 Nov 2008 |
DOIs | |
Publication status | Published - Mar 2009 |
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Keywords
- dimension reduction
- cointegration
- error correction-volatility factor model
- penalized goodness-of-fit criteria
- model selection
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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 journal › Article
TY - JOUR
T1 - Determining the number of factors in a multivariate error correction–volatility factor model
AU - Li, Q.
AU - Pan, J.
PY - 2009/3
Y1 - 2009/3
N2 - 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.
AB - 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.
KW - dimension reduction
KW - cointegration
KW - error correction-volatility factor model
KW - penalized goodness-of-fit criteria
KW - model selection
U2 - 10.1111/j.1368-423X.2008.00259.x
DO - 10.1111/j.1368-423X.2008.00259.x
M3 - Article
VL - 12
SP - 45
EP - 61
JO - Econometrics Journal
T2 - Econometrics Journal
JF - Econometrics Journal
SN - 1368-4221
IS - 1
ER -