Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1201220
- author
- Hacker, R. Scott and Hatemi-J, Abdulnasser LU
- organization
- publishing date
- 2008
- 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
- 2016-04-01 14:24:31
- date last changed
- 2022-03-06 19:07:31
@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}},
keywords = {{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}},
doi = {{10.1080/02664760801920473}},
volume = {{35}},
year = {{2008}},
}