Forecasting inflation with thick models and neural networks
AbstractThis paper applies linear and neural network-based “thick” models for forecasting inflation based on Phillips–curve formulations in the USA, Japan and the euro area. Thick models represent “trimmed mean” forecasts from several neural network models. They outperform the best performing linear models for “real-time” and “bootstrap” forecasts for service indices for the euro area, and do well, sometimes better, for the more general consumer and producer price indices across a variety of countries. JEL Classification: C12, E31
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Date of creation: Apr 2004
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Other versions of this item:
- McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-10-04 (All new papers)
- NEP-ECM-2005-10-04 (Econometrics)
- NEP-FOR-2005-10-04 (Forecasting)
- NEP-MAC-2005-10-04 (Macroeconomics)
- NEP-MON-2005-10-04 (Monetary Economics)
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