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Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH

Hacker, R. Scott and Hatemi-J, Abdulnasser LU (2008) In Journal of Applied Statistics 35(6). p.601-615
Abstract
The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag... (More)
The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
ARCH, stability, Monte Carlo simulations, information criteria, VAR, lag length
in
Journal of Applied Statistics
volume
35
issue
6
pages
601 - 615
publisher
Carfax Publishing
external identifiers
  • wos:000256403300001
  • scopus:46249118587
ISSN
0266-4763
DOI
10.1080/02664760801920473
language
English
LU publication?
yes
id
fbe615c3-52b3-4009-8cb5-0202b97e3186 (old id 1201220)
date added to LUP
2008-09-15 09:33:02
date last changed
2017-11-05 04:09:39
@article{fbe615c3-52b3-4009-8cb5-0202b97e3186,
  abstract     = {The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.},
  author       = {Hacker, R. Scott and Hatemi-J, Abdulnasser},
  issn         = {0266-4763},
  keyword      = {ARCH,stability,Monte Carlo simulations,information criteria,VAR,lag length},
  language     = {eng},
  number       = {6},
  pages        = {601--615},
  publisher    = {Carfax Publishing},
  series       = {Journal of Applied Statistics},
  title        = {Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH},
  url          = {http://dx.doi.org/10.1080/02664760801920473},
  volume       = {35},
  year         = {2008},
}