Predictable non-linearities in U.S. inflation

Jane M. Binner*, C. Thomas Elger, Birger Nilsson, Jonathan A. Tepper

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)323-328
Number of pages6
JournalEconomics Letters
Volume93
Issue number3
DOIs
Publication statusPublished - 1 Dec 2006

Bibliographical note

© 2006, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Inflation forecasting
  • Markov switching models
  • Recurrent neural networks

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  • Cite this

    Binner, J. M., Elger, C. T., Nilsson, B., & Tepper, J. A. (2006). Predictable non-linearities in U.S. inflation. Economics Letters, 93(3), 323-328. https://doi.org/10.1016/j.econlet.2006.06.001