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Shadow Rates as a Measure of the Monetary Policy Stance: Some International Evidence

Author

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  • Christina Anderl
  • Guglielmo Maria Caporale

Abstract

This paper examines the usefulness of shadow rates to measure the monetary policy stance by comparing them to the official policy rates and those implied by three types of Taylor rules in both inflation targeting countries (the UK, Canada, Australia and New Zealand) and others that have only targeted inflation at times (the US, Japan, the Euro Area and Switzerland) over the period from the early 1990s to December 2021. Shadow rates estimated from a dynamic factor model are shown to suggest a much looser policy stance than either the official policy rates or those implied by the Taylor rules, and generally to provide a more accurate picture of the monetary policy stance during both ZLB and non-ZLB periods, since they reflect the full range of unconventional policy measures used by central banks. Further, generalised impulse response analysis based on two alternative Vector Autoregression (VAR) models indicates that monetary shocks based on the shadow rates are more informative than those related to the official policy rates, especially during the Global Financial Crisis and the recent Covid-19 pandemic, when unconventional measures have been adopted.

Suggested Citation

  • Christina Anderl & Guglielmo Maria Caporale, 2022. "Shadow Rates as a Measure of the Monetary Policy Stance: Some International Evidence," CESifo Working Paper Series 9839, CESifo.
  • Handle: RePEc:ces:ceswps:_9839
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    References listed on IDEAS

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    1. Marco J. Lombardi & Feng Zhu, 2018. "A Shadow Policy Rate to Calibrate U.S. Monetary Policy at the Zero Lower Bound," International Journal of Central Banking, International Journal of Central Banking, vol. 14(5), pages 305-346, December.
    2. Christiano, Lawrence J & Eichenbaum, Martin & Evans, Charles, 1996. "The Effects of Monetary Policy Shocks: Evidence from the Flow of Funds," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 16-34, February.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    5. Sack, Brian & Wieland, Volker, 2000. "Interest-rate smoothing and optimal monetary policy: a review of recent empirical evidence," Journal of Economics and Business, Elsevier, vol. 52(1-2), pages 205-228.
    6. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    7. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-921, September.
    8. Gerlach, Stefan & Schnabel, Gert, 2000. "The Taylor rule and interest rates in the EMU area," Economics Letters, Elsevier, vol. 67(2), pages 165-171, May.
    9. Froyen, Richard T. & Guender, Alfred V., 2018. "The real exchange rate in Taylor rules: A Re-Assessment," Economic Modelling, Elsevier, vol. 73(C), pages 140-151.
    10. Jing Cynthia Wu & Fan Dora Xia, 2016. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 253-291, March.
    11. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    12. Christina Anderl & Guglielmo Maria Caporale, 2023. "Forecasting inflation with a zero lower bound or negative interest rates: Evidence from point and density forecasts," Manchester School, University of Manchester, vol. 91(3), pages 171-232, June.
    13. Wu, Jing Cynthia & Zhang, Ji, 2019. "A shadow rate New Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    14. Viktors Ajevskis, 2020. "The natural rate of interest: information derived from a shadow rate model," Applied Economics, Taylor & Francis Journals, vol. 52(47), pages 5129-5138, October.
    15. Rudebusch, Glenn D., 2002. "Term structure evidence on interest rate smoothing and monetary policy inertia," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1161-1187, September.
    16. Ben S. Bernanke & Michael T. Kiley & John M. Roberts, 2019. "Monetary Policy Strategies for a Low-Rate Environment," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 421-426, May.
    17. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
    18. Papadamou, Stephanos & Sidiropoulos, Moise & Vidra, Aristea, 2018. "A Taylor Rule for EU members. Does one rule fit to all EU member needs?," The Journal of Economic Asymmetries, Elsevier, vol. 18(C), pages 1-1.
    19. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    20. Leo Krippner, 2015. "A comment on Wu and Xia (2015), and the case for two-factor Shadow Short Rates," CAMA Working Papers 2015-48, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    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.
    22. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
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    Cited by:

    1. Evgenidis, Anastasios & Fasianos, Apostolos, 2023. "Modelling monetary policy’s impact on labour markets under Covid-19," Economics Letters, Elsevier, vol. 230(C).

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    More about this item

    Keywords

    dynamic factor models; shadow rates; inflation targeting; monetary policy stance;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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