Predictable non-linearities in U.S. inflation

This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a neural network and a Markov switching autoregressive (MS-AR) model. We find that predictable non-linearities in inflation are best accounted for by the MS-AR model.

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Bibliographic Details
Published inEconomics letters Vol. 93; no. 3; pp. 323 - 328
Main Authors Binner, Jane M., Elger, C. Thomas, Nilsson, Birger, Tepper, Jonathan A.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2006
Elsevier
SeriesEconomics Letters
Subjects
Applied economics
Economic forecasts
Economics
Economics and Business
Ekonomi och näringsliv
Inflation forecasting
Inflation rate
Markov switching models
Model testing
Nationalekonomi
networks
recurrent neural
Recurrent neural networks
Samhällsvetenskap
Social Sciences
C53
C52
Recurrent neural networks
C45
Markov switching models
Inflation forecasting
E37
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Summary:This paper compares the out-of-sample inflation forecasting performance of two non-linear models; a neural network and a Markov switching autoregressive (MS-AR) model. We find that predictable non-linearities in inflation are best accounted for by the MS-AR model.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0165-1765
1873-7374
DOI:10.1016/j.econlet.2006.06.001