Forecasting inflation with thick models and neural networks
AbstractThis paper applies linear and neural network-based âthickâ models for forecasting inflation based on Phillipsâcurve formulations in the USA, Japan and the euro area. Thick models represent âtrimmed meanâ forecasts from several neural network models. They outperform the best performing linear models for âreal-timeâ and âbootstrapâ 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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Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 22 (2005)
Issue (Month): 5 (September)
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Web page: http://www.elsevier.com/locate/inca/30411
Other versions of this item:
- McNelis, Paul & McAdam, Peter, 2004. "Forecasting inflation with thick models and neural networks," Working Paper Series 0352, European Central Bank.
- 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
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