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Estimating Monetary Policy Reaction Functions Using Quantile Regressions

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  • Wolters, Maik Hendrik

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. Realtime data of inflation forecasts and the output gap are used. I find significant and systematic variations of parameters over the conditional distribution of the interest rate.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 23857.

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Date of creation: 13 Jul 2010
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Handle: RePEc:pra:mprapa:23857
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Keywords: monetary policy rules; IV quantile regression; real-time data;

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Find related papers by JEL classification:
  • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
  • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
  • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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Cited by:

  1. 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.
  2. 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.
  3. 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).
  4. 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.
  5. Ravn, Søren Hove, 2014. "Asymmetric monetary policy towards the stock market: A DSGE approach," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 24-41.
  6. Schultefrankenfeld, Guido, 2010. "Forecast uncertainty and the Bank of England interest rate decisions," Discussion Paper Series 1: Economic Studies 2010,27, Deutsche Bundesbank, Research Centre.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. Luís Francisco Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, . "Analyzing the Taylor Rule with Wavelet Lenses," NIPE Working Papers 18/2014, NIPE - Universidade do Minho.

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