Bayesian VAR Models for Forecasting Irish Inflation

Geoff Kenny, Aidan Meyler and Terry Quinn

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper we focus on the development of multiple time series models for forecasting Irish Inflation. The Bayesian approach to the estimation of vector autoregressive (VAR) models is employed. This allows the estimated models combine the evidence in the data with any prior information which may also be available. A large selection of inflation indicators are assessed as potential candidates for inclusion in a VAR. The results confirm the significant improvement in forecasting performance which can be obtained by the use of Bayesian techniques. In general, however, forecasts of inflation contain a high degree of uncertainty. The results are also consistent with previous research in the Central Bank of Ireland which stresses a strong role for the exchange rate and foreign prices as a determinant of Irish prices.

Keywords: Bayesian; BVAR; inflation forecasts; Ireland (search for similar items in EconPapers)
JEL-codes: C32 C53 E30 (search for similar items in EconPapers)
Date: 1998-12
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

Published in Central Bank and Financial Services Authority of Ireland Technical Paper Series 4/RT/98.1998(1998): pp. 1-37

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/11360/1/MPRA_paper_11360.pdf original version (application/pdf)

Related works:
Working Paper: Bayesian VAR Models for Forecasting Irish Inflation (1998)
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:pra:mprapa:11360

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().