English, Article, Journal or magazine article edition: Does money matter in inflation forecasting? Jane M. Binner; Peter Tino; Jonathan Tepper; ...

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Bookmark: http://trove.nla.gov.au/version/27892
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Does money matter in inflation forecasting?
Author
  • Jane M. Binner
  • Peter Tino
  • Jonathan Tepper
  • Richard G. Anderson
  • Barry Jones
  • Graham Kendall
Physical Description
  • preprint
Notes
  • 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 non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques 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 naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.
  • Forecasting ; Inflation (Finance) ; Monetary theory
  • RePEc:fip:fedlwp:2009-030
Language
  • English
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