Forecasting Inflation: An Art as Well as a Science!
In this study, we build two forecasting models to predict inflation Harmonised Index of Consumer Prices (HICP) for the Netherlands and for the euro area. 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 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 aggregation method to predict total HICP inflation generally outperforms the direct method, except for long horizons in the case of the Netherlands.
Keywordsaggregation model selection time series models
JEL code(s)C32 C43 C52 C53
Unable to display preview. Download preview PDF.
- Benalal, N., J.L. Diaz del Hoyo, B. Landau, M. Roma and F. Skudelny (2004), ‘To Aggregate or not to Aggregate? Euro Area Inflation Forecasting,’ ECB Working Paper, 374.Google Scholar
- Banerjee, A., M. Marcellino and I. Masten (2003), ‘Leading Indicators for Euro Area Inflation and GDP Growth,’ CEPR Discussion Paper, 3893.Google Scholar
- Bruneau, C., O. de Bandt and A. Flageollet (2003a), ‘Forecasting Inflation in the Euro Area,’ Working Paper Banque de Franc, 102.Google Scholar
- Bruneau, C., O. de Bandt, A. Flageollet and E. Michaux (2003b), ‘Forecasting Inflation Using Economic Indicators: The Case of France,’ Working Paper Banque de France, 101.Google Scholar
- Christoffersen, P.F., Diebold, F.X. 1998‘Cointegration and Long-Horizon Forecasting’Journal of Business and Economic Statistics16450458Google Scholar
- Fritzer, F., G. Moser and J. Scharler (2002), ‘Forecasting Austrian HICP and its Components Using VAR and ARIMA models,’ Working Paper OENB, 73.Google Scholar
- Hendry, D.F. and G.E. Mizon (1999), ‘On Selecting Policy Analysis Models by Forecast Accuracy,’ University of Southampton Discussion Papers in Economics and Econometrics, 9918.Google Scholar
- Horowitz, J.L. 2001‘The BootstrapHeckman, J.J.Leamer, E.E. eds. Handbook of Econometrics,’Elsevier Science B.V.Amsterdam31593228Ch. 52Google Scholar
- Hubrich, K. (2001), Forecasting Euro Area Inflation: Does Contemporaneous Aggregation Improve the Forecasting Performance?,’ Research Memorandum WO&E, 661.Google Scholar
- Hubrich, K. 2005‘Forecasting Euro Area Inflation: Does Aggregating Forecasts by HICP Component Improve Forecast accuracy?’International Journal of Forecasting21119136Google Scholar
- Inoue, A. and L. Kilian (2006), ‘On the Selection of Forecasting Models,’ Journal of Econometrics, Forthcoming.Google Scholar
- Lütkepohl, H. 1987Forecasting Aggregated Vector ARMA ProcessesSpringer-VerlagBerlinGoogle Scholar
- Moser, G., F. Rumler and J. Scharler (2004), ‘Forecasting Austrian Inflation,’ Working Paper OENB, 91.Google Scholar
- Stock, J.H., Watson, M.W. 2003‘Forecasting Output and Inflation: The Role of Asset Prices’Journal of Economic LiteratureXLI788829Google Scholar
- Wallis, K.F. 1999‘Asymmetric Density Forecasts of Inflation and the Bank of England’s Fan Chart’National Institute Economic Review167106112Google Scholar