A Statistical Forecasting Method for Inflation Forecasting: Hitting Every Vector Autoregression and Forecasting under Model Uncertainty

Ippei Fujiwara and Maiko Koga

Monetary and Economic Studies, 2004, vol. 22, issue 1, 123-142

Abstract: Typically, when conducting econometric forecasting, estimation is carried out on a forecasting model that is built upon some assumed economic structure. However, such techniques cannot avoid running into the possibility of misspecification, which will occur should there be some error in the assumptions underlying this economic structure. In this paper, in which we concentrate upon inflation forecasting, we present a method of hitting every vector autoregression (VAR) and forecasting under model uncertainty (HEVAR/FMU) that stresses statistical relationships among time-series data, and that makes no structural assumptions, other than to set up the underlying variables. Use of this HEVAR/FMU, in addition to establishing a more objective setting and enabling us to produce forecasts that take uncertainty into account, gives better results when forecasting qualitative movements in inflation. Therefore, we can state that the HEVAR/FMU can also play a valuable role in providing a cross-check for forecasts produced using such structural-type models.

JEL-codes: C53 E31 E37 (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.imes.boj.or.jp/research/papers/english/me22-1-5.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ime:imemes:v:22:y:2004:i:1:p:123-142

Access Statistics for this article

More articles in Monetary and Economic Studies from Institute for Monetary and Economic Studies, Bank of Japan Contact information at EDIRC.
Bibliographic data for series maintained by Kinken ().