Forecasting inflation: An art as well as a science!

Peter Vlaar () and Ard Reijer ()

No 148, Computing in Economics and Finance 2004 from Society for Computational Economics

Abstract: In this study we build two forecasting models to predict inflation for the Netherlands and for the euro area. Inflation is the yearly change of the Harmonised Index of Consumer Prices (HICP). The models provide point forecasts and prediction intervals for both the components of the HICP and the aggregated HICP-index itself. Both models are small-scale linear time series models allowing for long run equilibrium relationships between HICP components and other variables, notably the hourly wage rate and the import or producer prices. The model for the Netherlands is used to generate the Dutch inflation projections over a horizon of 11-15 months ahead for the eurosystem’s Narrow Inflation Projection Exercise (NIPE). The recursive forecast errors for several forecast horizons are evaluated for all models, and are found to outperform a naive forecast and optimal AR models. Moreover, the same result holds for the Dutch NIPE projections, which have been provided quarterly since 1999. The direct and aggregation methods to predict total HICP inflation perform about equally good

Keywords: model selection; time series models; aggregation (search for similar items in EconPapers)
JEL-codes: C32 C43 C52 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-cba, nep-ets and nep-ifn
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http://repec.org/sce2004/up.22432.1077785696.pdf (application/pdf)

Related works:
Journal Article: Forecasting Inflation: An Art as Well as a Science! (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:148

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