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Forecasting Inflation with Thick Models and Neural Networks

McAdam, Peter, McNelis, Paul (2005) Forecasting Inflation with Thick Models and Neural Networks. Economic Modelling, 22 (5). pp. 848-867. ISSN 0264-9993. (doi:10.1016/j.econmod.2005.06.002) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:9426)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1016/j.econmod.2005.06.002

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. (c) 2005 Elsevier B.V All rights reserved

Item Type: Article
DOI/Identification number: 10.1016/j.econmod.2005.06.002
Subjects: H Social Sciences
Divisions: Divisions > Division of Human and Social Sciences > School of Economics
Depositing User: G.F. Green
Date Deposited: 08 Oct 2008 17:31 UTC
Last Modified: 16 Nov 2021 09:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/9426 (The current URI for this page, for reference purposes)

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