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Estimating monetary policy reaction functions using quantile regressions

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  • Wolters, Maik H.

Abstract

Monetary policy rule parameters are usually estimated at the mean of the interest rate distribution conditional on inflation and an output gap. This is an incomplete description of monetary policy reactions when the parameters are not uniform over the conditional distribution of the interest rate. I use quantile regressions to estimate parameters over the whole conditional distribution of the federal funds rate. Inverse quantile regressions are applied to deal with endogeneity. Real-time data of inflation forecasts and the output gap are used. I find significant and systematic variations of parameters over the conditional interest rate distribution. Testing for structural changes in regression quantiles shows that these parameter variations cannot be explained by preference shifts of the Fed. Asymmetric interest rate responses can rather be related to expansions and recessions and are consistent with a recession avoidance preference of the Fed during the Volcker–Greenspan era.

Suggested Citation

  • Wolters, Maik H., 2012. "Estimating monetary policy reaction functions using quantile regressions," Journal of Macroeconomics, Elsevier, vol. 34(2), pages 342-361.
  • Handle: RePEc:eee:jmacro:v:34:y:2012:i:2:p:342-361
    DOI: 10.1016/j.jmacro.2011.12.004
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    Cited by:

    1. Sebastian Gechert & Ansgar Rannenberg, 2014. "Are Fiscal Multipliers Regime-Dependent? A Meta Regression Analysis," IMK Working Paper 139-2014, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    2. Kiesel, Konstantin & Wolters, Maik H., 2014. "Estimating monetary policy rules when the zero lower bound on nominal interest rates is approached," Kiel Working Papers 1898, Kiel Institute for the World Economy (IfW).
    3. Luís Francisco Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, "undated". "Analyzing the Taylor Rule with Wavelet Lenses," NIPE Working Papers 18/2014, NIPE - Universidade do Minho.
    4. Schultefrankenfeld Guido, 2013. "Forecast uncertainty and the Bank of England’s interest rate decisions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(1), pages 1-20, February.
    5. Tae-Hwan Kim & Christophe Muller, 2017. "A Robust Test of Exogeneity Based on Quantile Regressions," Working Papers halshs-01508067, HAL.
    6. Tae-Hwan Kim & Christophe Muller, 2013. "A Test for Endogeneity in Conditional Quantiles," AMSE Working Papers 1342, Aix-Marseille School of Economics, Marseille, France, revised Aug 2013.
    7. Shen, Chung-Hua & Lin, Kun-Li & Guo, Na, 2016. "Hawk or dove: Switching regression model for the monetary policy reaction function in China," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 94-111.
    8. Luís Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2016. "Estimating the Taylor Rule in the Time-Frequency Domain," CEF.UP Working Papers 1404, Universidade do Porto, Faculdade de Economia do Porto.
    9. repec:spr:empeco:v:53:y:2017:i:4:d:10.1007_s00181-016-1195-0 is not listed on IDEAS
    10. Neuenkirch, Matthias & Tillmann, Peter, 2014. "Inflation targeting, credibility, and non-linear Taylor rules," Journal of International Money and Finance, Elsevier, vol. 41(C), pages 30-45.
    11. Gabriela Bezerra De Medeiros & Marcelo Savino Portugal & Edilean Kleber Da Silva Bejarano Aragon, 2016. "Endogeneity And Nonlinearities In Central Bank Of Brazil’S Reaction Functions: An Inverse Quantile Regression Approach," Anais do XLIII Encontro Nacional de Economia [Proceedings of the 43rd Brazilian Economics Meeting] 061, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    12. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Won Joong Kim, 2017. "Monetary Policy Reaction Functions of the TICKs: A Quantile Regression Approach," Working Papers 201738, University of Pretoria, Department of Economics.
    13. Apergis, Nicholas & Christou, Christina, 2015. "The behaviour of the bank lending channel when interest rates approach the zero lower bound: Evidence from quantile regressions," Economic Modelling, Elsevier, vol. 49(C), pages 296-307.
    14. William Miles & Sam Schreyer, 2012. "Is monetary policy non-linear in Indonesia, Korea, Malaysia, and Thailand? A quantile regression analysis," Asian-Pacific Economic Literature, Asia Pacific School of Economics and Government, The Australian National University, vol. 26(2), pages 155-166, November.
    15. Ravn, Søren Hove, 2014. "Asymmetric monetary policy towards the stock market: A DSGE approach," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 24-41.
    16. Jau-er Chen & Masanori Kashiwagi, 2017. "The Japanese Taylor rule estimated using censored quantile regressions," Empirical Economics, Springer, vol. 52(1), pages 357-371, February.

    More about this item

    Keywords

    Monetary policy rules; IV quantile regression; Real-time data; Asymmetries; Policy preferences;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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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