文章基本信息

标题：Forecasting inflation with thick models and neural networks
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作者：Paul McNelis ; Peter McAdam
期刊名称：Euro Area Balance of Payments and International Investment Position Statistics
印刷版ISSN：1830-3420
电子版ISSN：1830-3439
出版年度：2004
出版社：European Central Bank
摘要：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.
关键词：Neural Networks; Thick Models; Phillips curves; real-time;forecasting; bootstrap.