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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.

Suggested Citation

  • George Monokroussos, 2006. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Discussion Papers 06-02, University at Albany, SUNY, Department of Economics.
    • Handle: RePEc:nya:albaec:06-02
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    Cited by:

    1. Gurnain Kaur Pasricha, 2017. "Policy Rules for Capital Controls," BIS Working Papers 670, Bank for International Settlements.
    2. Eric Girardin & Sandrine Lunven & Guonan Ma, 2014. "Inflation and China's monetary policy reaction function: 2002-2013," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation, inflation and monetary policy in Asia and the Pacific, volume 77, pages 159-170, Bank for International Settlements.
    3. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    4. Seibert, Armin & Sirchenko, Andrei & Müller, Gernot, 2021. "A model for policy interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 124(C).
    5. Eric Girardin & Sandrine Lunven & Guonan Ma, 2017. "China's evolving monetary policy rule: from inflation-accommodating to anti-inflation policy," BIS Working Papers 641, Bank for International Settlements.
    6. van den Hauwe, Sjoerd & Paap, Richard & van Dijk, Dick, 2013. "Bayesian forecasting of federal funds target rate decisions," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 19-40.
    7. George Monokroussos, 2006. "A Dynamic Tobit Model for the Open Market Desk's Daily Reaction Function," Computing in Economics and Finance 2006 390, Society for Computational Economics.
    8. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    9. 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.
    10. Andrei Sirchenko, 2019. "A regime-switching model for the federal funds rate target," UvA-Econometrics Working Papers 19-01, Universiteit van Amsterdam, Dept. of Econometrics.
    11. Hyeongwoo Kim & Wen Shi, 2014. "The Determinants of the Benchmark Interest Rates in China: A Discrete Choice Model Approach," Auburn Economics Working Paper Series auwp2014-12, Department of Economics, Auburn University.
    12. George Monokroussos, 2013. "A Classical MCMC Approach to the Estimation of Limited Dependent Variable Models of Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 71-105, June.
    13. Zhang, Xinyu & Lu, Zudi & Zou, Guohua, 2013. "Adaptively combined forecasting for discrete response time series," Journal of Econometrics, Elsevier, vol. 176(1), pages 80-91.
    14. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    15. Kim, Hyeongwoo & Shi, Wen, 2018. "The determinants of the benchmark interest rates in China," Journal of Policy Modeling, Elsevier, vol. 40(2), pages 395-417.
    16. repec:syb:wpbsba:01/2013 is not listed on IDEAS
    17. Pasricha, Gurnain K., 2022. "Estimated policy rules for capital controls," Journal of International Money and Finance, Elsevier, vol. 122(C).

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • 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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