Impulse response analysis in vector autoregressions with unknown lag order
dc.contributor.author | Kilian, Lutz | en_US |
dc.date.accessioned | 2006-04-19T13:54:13Z | |
dc.date.available | 2006-04-19T13:54:13Z | |
dc.date.issued | 2001-04 | en_US |
dc.identifier.citation | Kilian, Lutz (2001)."Impulse response analysis in vector autoregressions with unknown lag order." Journal of Forecasting 20(3): 161-179. <http://hdl.handle.net/2027.42/34851> | en_US |
dc.identifier.issn | 0277-6693 | en_US |
dc.identifier.issn | 1099-131X | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/34851 | |
dc.description.abstract | We show that the effects of overfitting and underfitting a vector autoregressive (VAR) model are strongly asymmetric for VAR summary statistics involving higher-order dynamics (such as impulse response functions, variance decompositions, or long-run forecasts) . Underfit models often underestimate the true dynamics of the population process and may result in spuriously tight confidence intervals. These insights are important for applied work, regardless of how the lag order is determined. In addition, they provide a new perspective on the trade-offs between alternative lag order selection criteria. We provide evidence that, contrary to conventional wisdom, for many statistics of interest to VAR users the point and interval estimates based on the AIC compare favourably to those based on the more parsimonious Schwarz Information Criterion and Hannan – Quinn Criterion. Copyright © 2001 John Wiley & Sons, Ltd. | en_US |
dc.format.extent | 206775 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | Business, Finance & Management | en_US |
dc.title | Impulse response analysis in vector autoregressions with unknown lag order | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Philosophy | en_US |
dc.subject.hlbsecondlevel | Social Sciences (General) | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbtoplevel | Humanities | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | University of Michigan, USA, and CEPR, UK ; Department of Economics, University of Michigan, Ann Arbor, MI 48109-1220, USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/34851/1/770_ftp.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1002/1099-131X(200104)20:3<161::AID-FOR770>3.0.CO;2-X | en_US |
dc.identifier.source | Journal of Forecasting | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.