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Does money matter in inflation forecasting?

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

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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  • Richard G. Anderson & Jane M. Binner & Barry E. Jones & Graham Kendall & Jonathan Tepper & Peter Tino, 2009. "Does money matter in inflation forecasting?," Working Papers 2009-030, Federal Reserve Bank of St. Louis.
  • Handle: RePEc:fip:fedlwp:2009-030
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    2. Jane M. Binner & logan J. Kelly, 2017. "Modelling Money Shocks in a Small Open Economy: The Case of Taiwan," Manchester School, University of Manchester, vol. 85, pages 104-120, September.
    3. 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.
    4. Rizvi, Syed Kumail Abbas & Naqvi, Bushra, 2009. "Inflation Volatility: An Asian Perspective," MPRA Paper 19489, University Library of Munich, Germany.
    5. Egorov D.A. (Егоров, Д.А.) & Perevyshina E.A. (Перевышина, Е.А.), 2016. "Modelling of Inflationary Processes in Russia [Моделирование Инфляционных Процессов В России]," Working Papers 2138, Russian Presidential Academy of National Economy and Public Administration.
    6. Anderson, Richard G. & Binner, Jane M. & Schmidt, Vincent A., 2012. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Economics Letters, Elsevier, vol. 117(1), pages 174-177.
    7. Lahmiri, Salim, 2016. "Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 388-396.
    8. 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.
    9. Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.
    10. Erick Lahura, 2017. "Monetary Aggregates and Monetary Policy in Peru," BCAM Working Papers 1704, Birkbeck Centre for Applied Macroeconomics.

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    Forecasting; Inflation (Finance); Monetary theory;
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