Abstract
This paper explores the asymptotic distribution of the cointegrating vector estimator in error correction models with conditionally heteroskedastic errors. Asymptotic properties of the maximum likelihood estimator (MLE) of the cointegrating vector, which estimates the cointegrating vector and the multivariate GARCH process jointly, are provided. The MLE of the cointegrating vector follows mixture normal, and its asymptotic distribution depends on the conditional heteroskedasticity and the kurtosis of standardized innovations. The reduced rank regression (RRR) estimator and the regression-based cointegrating vector estimators do not consider conditional heteroskedasticity, and thus the efficiency gain of the MLE emerges as the magnitude of conditional heteroskedasticity increases. The simulation results indicate that the relative power of the t-statistics based on the MLE improves significantly as the GARCH effect increases.
| Original language | English |
|---|---|
| Pages (from-to) | 68-111 |
| Number of pages | 44 |
| Journal | Journal of Econometrics |
| Volume | 137 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2007 Mar 1 |
| Externally published | Yes |
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Keywords
- Cointegrating vector
- Efficiency gain
- Multivariate GARCH
ASJC Scopus subject areas
- Economics and Econometrics
- Finance
- Statistics and Probability
Cite this
Asymptotic distribution of the cointegrating vector estimator in error correction models with conditional heteroskedasticity. / Seo, Byeongseon.
In: Journal of Econometrics, Vol. 137, No. 1, 01.03.2007, p. 68-111.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Asymptotic distribution of the cointegrating vector estimator in error correction models with conditional heteroskedasticity
AU - Seo, Byeongseon
PY - 2007/3/1
Y1 - 2007/3/1
N2 - This paper explores the asymptotic distribution of the cointegrating vector estimator in error correction models with conditionally heteroskedastic errors. Asymptotic properties of the maximum likelihood estimator (MLE) of the cointegrating vector, which estimates the cointegrating vector and the multivariate GARCH process jointly, are provided. The MLE of the cointegrating vector follows mixture normal, and its asymptotic distribution depends on the conditional heteroskedasticity and the kurtosis of standardized innovations. The reduced rank regression (RRR) estimator and the regression-based cointegrating vector estimators do not consider conditional heteroskedasticity, and thus the efficiency gain of the MLE emerges as the magnitude of conditional heteroskedasticity increases. The simulation results indicate that the relative power of the t-statistics based on the MLE improves significantly as the GARCH effect increases.
AB - This paper explores the asymptotic distribution of the cointegrating vector estimator in error correction models with conditionally heteroskedastic errors. Asymptotic properties of the maximum likelihood estimator (MLE) of the cointegrating vector, which estimates the cointegrating vector and the multivariate GARCH process jointly, are provided. The MLE of the cointegrating vector follows mixture normal, and its asymptotic distribution depends on the conditional heteroskedasticity and the kurtosis of standardized innovations. The reduced rank regression (RRR) estimator and the regression-based cointegrating vector estimators do not consider conditional heteroskedasticity, and thus the efficiency gain of the MLE emerges as the magnitude of conditional heteroskedasticity increases. The simulation results indicate that the relative power of the t-statistics based on the MLE improves significantly as the GARCH effect increases.
KW - Cointegrating vector
KW - Efficiency gain
KW - Multivariate GARCH
UR - http://www.scopus.com/inward/record.url?scp=33846487372&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33846487372&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2006.03.008
DO - 10.1016/j.jeconom.2006.03.008
M3 - Article
VL - 137
SP - 68
EP - 111
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 1
ER -