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Does money matter in inflation forecasting?

Listed author(s):
  • Jane M. Binner
  • Peter Tino
  • Jonathan Tepper
  • Richard G. Anderson
  • Barry E. Jones
  • Graham Kendall

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.

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Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2009-030.

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Date of creation: 2009
Handle: RePEc:fip:fedlwp:2009-030
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