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Does money matter in inflation forecasting?

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  • Binner, J.M.
  • Tino, P.
  • Tepper, J.
  • Anderson, R.
  • Jones, B.
  • Kendall, G.

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 nonlinear 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 naïve random walk model. The best models were nonlinear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. Beyond its economic findings, our study is in the tradition of physicists’ long-standing interest in the interconnections among statistical mechanics, neural networks, and related nonparametric statistical methods, and suggests potential avenues of extension for such studies.

Suggested Citation

  • Binner, J.M. & Tino, P. & Tepper, J. & Anderson, R. & Jones, B. & Kendall, G., 2010. "Does money matter in inflation forecasting?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4793-4808.
  • Handle: RePEc:eee:phsmap:v:389:y:2010:i:21:p:4793-4808
    DOI: 10.1016/j.physa.2010.06.015
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    Cited by:

    1. 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.
    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. Egorov D.A. & Perevyshina E.A., 2016. "Modelling of Inflationary Processes in Russia," Working Papers 2138, Russian Presidential Academy of National Economy and Public Administration.
    5. 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.
    6. Lahura, Erick, 2017. "Monetary Aggregates and Monetary Policy in Peru," Working Papers 2017-003, Banco Central de Reserva del Perú.
    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. repec:bla:manchs:v:85:y:2017:i::p:104-120 is not listed on IDEAS

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