Forecasting irish inflation using ARIMA models

Aidan Meyler, Geoff Kenny and Terry Quinn

MPRA Paper from University Library of Munich, Germany

Abstract: This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models - the Box Jenkins approach and the objective penalty function methods. The emphasis is on forecast performance which suggests more focus on minimising out-of-sample forecast errors than on maximising in-sample ‘goodness of fit’. Thus, the approach followed is unashamedly one of ‘model mining’ with the aim of optimising forecast performance. Practical issues in ARIMA time series forecasting are illustrated with reference to the harmonised index of consumer prices (HICP) and some of its major sub-components.

Keywords: NAIRU; inflation; unobserved components; kalman filter (search for similar items in EconPapers)
JEL-codes: C22 C53 C61 C62 E00 E37 (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 (48)

Published in Central Bank and Financial Services Authority of Ireland Technical Paper Series 3/RT/98.1998(1998): pp. 1-48

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Related works:
Working Paper: Forecasting Irish inflation using ARIMA models (1998)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:11359

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