A Practitioner's Guide to Lag Order Selection For VAR Impulse Response Analysis
It is common in empirical macroeconomics to fit vector autoregressive (VAR) models to construct estimates of impulse responses. An important preliminary step in impulse response analysis is the selection of the VAR lag order. In this paper, we compare the six lag-order selection criteria most commonly used in applied work. Our metric is the mean-squared error (MSE) of the implied pointwise impulse response estimates normalized relative to their MSE based on knowing the true lag order. Based on our simulation design we conclude that for monthly VAR models, the Akaike Information Criterion (AIC) tends to produce the most accurate structural and semi-structural impulse response estimates for realistic sample sizes. For quarterly VAR models, the Hannan-Quinn Criterion (HQC) appears to be the most accurate criterion with the exception of sample sizes smaller than 120, for which the Schwarz Information Criterion (SIC) is more accurate. For persistence profiles based on quarterly vector error correction models with known cointegrating vector, our results suggest that the SIC is the most accurate criterion for all realistic sample sizes.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 9 (2005)
Issue (Month): 1 (March)
|Contact details of provider:|| Web page: https://www.degruyter.com|
|Order Information:||Web: https://www.degruyter.com/view/j/snde|
When requesting a correction, please mention this item's handle: RePEc:bpj:sndecm:v:9:y:2005:i:1:n:2. 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: (Peter Golla)
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.