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Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy

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  • George Monokroussos

Abstract

I estimate, using real-time data, a forward-looking, dynamic, discrete-choice monetary policy reaction function for the US economy, that accounts for the fact that there are substantial restrictions in the period-to-period changes of the Fed's policy instrument (an issue which is however largely ignored in the existing literature). I overcome ensuing computational complications by estimating the model using Markov Chain Monte Carlo methods. I find a substantial contrast between the periods before and after Paul Volcker's appointment as Fed Chairman in 1979, both in terms of the Fed's response to expected inflation and in terms of its response to the (perceived) output gap: In the pre-Volcker era the Fed's response to inflation was substantially weaker than in the Volcker-Greenspan era; conversely, the Fed seems to have been more responsive to (inaccurate real-time estimates of) the output gap in the pre-Volcker era than later. These results, which carry through a series of extensions and robustness checks, provide support for the "policy mistakes" hypothesis (according to which the pre-Volcker Fed made mistakes in its conduct of monetary policy, and starting with Volcker's appointment the Fed avoided to a substantial extent mistaken practices of the past) as an explanation of the stark contrast in US macroeconomic performance between the pre-Volcker and the Volcker/Greenspan periods.

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Paper provided by University at Albany, SUNY, Department of Economics in its series Discussion Papers with number 06-02.

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Date of creation: 2006
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Handle: RePEc:nya:albaec:06-02

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
Phone: (518) 442-4735
Fax: (518) 442-4736

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Postal: Department of Economics, BA 110 University at Albany State University of New York Albany, NY 12222 U.S.A.
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Cited by:
  1. Sjoerd van den Hauwe & Dick van Dijk & Richard Paap, 2011. "Bayesian Forecasting of Federal Funds Target Rate Decisions," Tinbergen Institute Discussion Papers 11-093/4, Tinbergen Institute.
  2. Sjoerd van den Hauwe & Dick van Dijk & Richard Paap, 2011. "Bayesian Forecasting of Federal Funds Target Rate Decisions," Tinbergen Institute Discussion Papers 11-093/4, Tinbergen Institute.
  3. Dick van Dijk & Robin L. Lumsdaine & Michel van der Wel, 2014. "Market Set-Up in Advance of Federal Reserve Policy Decisions," NBER Working Papers 19814, National Bureau of Economic Research, Inc.
  4. George Monokroussos, 2013. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 42(1), pages 71-105, June.
  5. Kajal Lahiri & Liu Yang, 2012. "Forecasting Binary Outcomes," Discussion Papers, University at Albany, SUNY, Department of Economics 12-09, University at Albany, SUNY, Department of Economics.
  6. George Monokroussos, 2006. "A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function," Computing in Economics and Finance 2006, Society for Computational Economics 390, Society for Computational Economics.
  7. Zhang, Xinyu & Lu, Zudi & Zou, Guohua, 2013. "Adaptively combined forecasting for discrete response time series," Journal of Econometrics, Elsevier, Elsevier, vol. 176(1), pages 80-91.
  8. repec:syb:wpbsba:01/2013 is not listed on IDEAS

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