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Theses, dissertations and research publications (including journal articles, conference abstracts and books) from Lund University are collected in this database. Where possible, the option to download a full text document is available. It is also possible to search for Lund University student theses in the student theses database.
| Title | Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH |
| Author/s | R. Scott Hacker, Abdulnasser Hatemi-J |
| Department/s |
Department of Statistics
|
| Full-text | Full text is not available in this archive |
| Alternative location (URL) | http://dx.doi.org/10.1080/0266... Restricted Access (Alternative Location) |
| Publication/Series | Journal of Applied Statistics |
| Publishing year | 2008 |
| Volume | 35 |
| Issue | 6 |
| Pages | 601 - 615 |
| Document type | Journal article |
| Status | published |
| Quality controlled | yes |
| Language | English |
| Publisher | Routledge |
| Abstract English | 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. |
| Subject |
Mathematics and Statistics |
| Keywords | stability, ARCH, Monte Carlo simulations, information criteria, VAR, lag length |
| ISBN/ISSN/Other |
ISSN: 0266-4763 |
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