Lag-length Selection in VAR-models Using Equal and Unequal Lag-Length Procedures
It is well known that inference in vector autoregressive models depends crucially on the choice of lag-length. Various lag-length selection procedures have been suggested and evaluated in the literature. In these evaluations the possibility that the true model may have unequal lag-length has, however, received little attention. In this paper we investigate how sensitive lag-length estimation procedures, based on assumptions of equal or unequal lag-lengths, are to the true model structure. The procedures used in the paper are based on information criteria and we give results for AIC, HQ and BIC. In the Monte Carlo study we generate data from a variety of VAR-models with properties similar to macro-economic time-series. We find that the commonly used procedure based on equal lag-length together with AIC and HQ performs well in most cases. The procedure (due to Hsiao) allowing for unequal lag-lengths produce reasonable results when the true model has unequal lag-length. The Hsiao procedure also tend to do better in models with a more complicated lag structure.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||07 Jun 1997|
|Publication status:||Published in Computational Statistics, 1999, pages 171-187.|
|Contact details of provider:|| Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden|
Phone: +46-(0)8-736 90 00
Fax: +46-(0)8-31 01 57
Web page: http://www.hhs.se/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:hhs:hastef:0177. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helena Lundin)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.