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Does money matter in inflation forecasting?

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Author Info

  • Jane M. Binner
  • Peter Tino
  • Jonathan Tepper
  • Richard G. Anderson
  • Barry Jones
  • Graham Kendall

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.

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Bibliographic Info

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
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Handle: RePEc:fip:fedlwp:2009-030

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Keywords: Forecasting ; Inflation (Finance) ; Monetary theory;

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References

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Citations

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Cited by:
  1. Richard G. Anderson & Jane M. Binner & Vincent A. Schmidt, 2011. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Working Papers 2011-007, Federal Reserve Bank of St. Louis.
  2. Rizvi, Syed Kumail Abbas & Naqvi, Bushra, 2009. "Inflation Volatility: An Asian Perspective," MPRA Paper 19489, University Library of Munich, Germany.
  3. Horváth, Roman & Komárek, Luboš & Rozsypal, Filip, 2011. "Does money help predict inflation? An empirical assessment for Central Europe," Economic Systems, Elsevier, vol. 35(4), pages 523-536.
  4. Mulligan, Robert F., 2013. "A sectoral analysis of the financial instability hypothesis," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(4), pages 450-459.
  5. Periklis Gogas & Theophilos Papadimitriou & Elvira Takli, 2013. "Comparison of simple sum and Divisia monetary aggregates in GDP forecasting: a support vector machines approach," Economics Bulletin, AccessEcon, vol. 33(2), pages 1101-1115.

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