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Forecasting inflation with thick models and neural networks

Author

Listed:
  • McAdam, Peter
  • McNelis, Paul

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

Suggested Citation

  • McAdam, Peter & McNelis, Paul, 2004. "Forecasting inflation with thick models and neural networks," Working Paper Series 352, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:2004352
    Note: 50336
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • 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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