Forecasting irish inflation using ARIMA models
Geoff Kenny and
MPRA Paper from University Library of Munich, Germany
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: C53 C61 C22 E00 E37 C62 (search for similar items in EconPapers)
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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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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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