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Predicting Monetary Policy Using Artificial Neural Networks

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  • Hinterlang, Natascha

Abstract

This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve's Greenbook real-time data. The results indicate that articial neural networks are able to predict the nominal interest rate better than linear and nonlinear Taylor rule models as well as univariate processes. While in-sample measures usually imply a forward-looking behaviour of the central bank, using nowcasts of the explanatory variables seems to be better suited for forecasting purposes. Overall, evidence suggests that U.S. monetary policy behaviour between 1987-2012 is nonlinear.

Suggested Citation

  • Hinterlang, Natascha, 2019. "Predicting Monetary Policy Using Artificial Neural Networks," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203503, Verein für Socialpolitik / German Economic Association.
  • Handle: RePEc:zbw:vfsc19:203503
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    References listed on IDEAS

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    1. Dolado Juan & Pedrero Ramón María-Dolores & Ruge-Murcia Francisco J., 2004. "Nonlinear Monetary Policy Rules: Some New Evidence for the U.S," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(3), pages 1-34, September.
    2. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(1), pages 147-180.
    3. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    4. Steven Gonzalez, "undated". "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
    5. Athanasios Orphanides, 2001. "Monetary Policy Rules Based on Real-Time Data," American Economic Review, American Economic Association, vol. 91(4), pages 964-985, September.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Athanasios Orphanides & Volker W. Wieland, 2008. "Economic projections and rules of thumb for monetary policy," Review, Federal Reserve Bank of St. Louis, vol. 90(Jul), pages 307-324.
    8. Kenneth Petersen, 2007. "Does the Federal Reserve Follow a Non-Linear Taylor Rule?," Working papers 2007-37, University of Connecticut, Department of Economics.
    9. Clarida, Richard & Gali, Jordi & Gertler, Mark, 1998. "Monetary policy rules in practice Some international evidence," European Economic Review, Elsevier, vol. 42(6), pages 1033-1067, June.
    10. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    11. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    12. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    13. A. Robert Nobay & David A. Peel, 2003. "Optimal Discretionary Monetary Policy in a Model of Asymmetric Central Bank Preferences," Economic Journal, Royal Economic Society, vol. 113(489), pages 657-665, July.
    14. Ruge-Murcia, Francisco J., 2004. "The inflation bias when the central bank targets the natural rate of unemployment," European Economic Review, Elsevier, vol. 48(1), pages 91-107, February.
    15. Dolado, Juan J. & Maria-Dolores, Ramon & Naveira, Manuel, 2005. "Are monetary-policy reaction functions asymmetric?: The role of nonlinearity in the Phillips curve," European Economic Review, Elsevier, vol. 49(2), pages 485-503, February.
    16. Schaling, Eric, 2004. "The Nonlinear Phillips Curve and Inflation Forecast Targeting: Symmetric versus Asymmetric Monetary Policy Rules," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(3), pages 361-386, June.
    17. Vítor, Castro, 2011. "Can central banks' monetary policy be described by a linear (augmented) Taylor rule or by a nonlinear rule?," Journal of Financial Stability, Elsevier, vol. 7(4), pages 228-246, December.
    18. Qin, Ting & Enders, Walter, 2008. "In-sample and out-of-sample properties of linear and nonlinear Taylor rules," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 428-443, March.
    19. Cukierman Alex & Muscatelli Anton, 2008. "Nonlinear Taylor Rules and Asymmetric Preferences in Central Banking: Evidence from the United Kingdom and the United States," The B.E. Journal of Macroeconomics, De Gruyter, vol. 8(1), pages 1-31, February.
    20. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    21. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
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    More about this item

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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