Forecasting inflation: An art as well as a science!

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 goodmodel selection, time series models, aggregation