Forecasting inflation with thick models and neural networks

Peter McAdam () and Paul McNelis ()

No 352, Working Paper Series from European Central Bank

Abstract: This 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

Keywords: bootstrap.; Neural Networks; Phillips Curves; real-time forecasting; Thick Models (search for similar items in EconPapers)
Date: 2004-04
Note: 50336
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Citations: View citations in EconPapers (18)

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Journal Article: Forecasting inflation with thick models and neural networks (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:2004352

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