Does money matter in inflation forecasting?

J.M. Binner, P. Tiño, J. Tepper, R. Anderson, B. Jones, G. Kendall

Research output: Contribution to journalArticlepeer-review


This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two nonlinear techniques, namely, recurrent neural networks and kernel recursive least squares regressiontechniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a nave random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists' long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

Original languageEnglish
Pages (from-to)4793-4808
Number of pages16
JournalPhysica A
Issue number21
Early online date8 Jul 2010
Publication statusPublished - 1 Nov 2010

Bibliographical note

© 2010, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


  • inflation
  • Kernel methods
  • monetary aggregates
  • recurrent neural networks


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