Does money matter in inflation forecasting?

Jane M. Binner, Peter Tino, Jonathan Tepper, Richard G. Anderson, Barry Edward Jones () and Graham Kendall

No 2009-030, Working Papers from Federal Reserve Bank of St. Louis

Abstract: 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.

Keywords: Forecasting; Inflation (Finance); Monetary theory (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-for, nep-mac and nep-mon
Date: 2009
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Persistent link: http://EconPapers.repec.org/RePEc:fip:fedlwp:2009-030

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