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Identifying the Stance of Monetary Policy at the Zero Lower Bound: A Markov-switching Estimation Exploiting Monetary-Fiscal Policy Interdependence

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Abstract

In this paper, I propose an econometric technique to estimate a Markov-switching Taylor rule subject to the zero lower bound of interest rates. I show that incorporating a Tobit-like specification allows to obtain consistent estimators. More importantly, I show that linking the switching of the Taylor rule coefficients to the switching of the coefficients of an auxiliary uncensored Markov-switching regression improves the identification of an otherwise unidentifiable prevalent monetary regime. To illustrate the proposed estimation technique, I use U.S. quarterly data spanning 1960:1-2013:4. The chosen auxiliary Markov-switching regression is a fiscal policy rule where federal revenues react to debt and the output gap. Results show that there is evidence of policy co-movements with debt-stabilizing fiscal policy more likely accompanying active monetary policy, and vice versa.

Suggested Citation

  • Manuel Gonzalez-Astudillo, 2014. "Identifying the Stance of Monetary Policy at the Zero Lower Bound: A Markov-switching Estimation Exploiting Monetary-Fiscal Policy Interdependence," Finance and Economics Discussion Series 2014-97, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2014-97
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    Cited by:

    1. Caporale, Guglielmo Maria & Helmi, Mohamad Husam & Çatık, Abdurrahman Nazif & Menla Ali, Faek & Akdeniz, Coşkun, 2018. "Monetary policy rules in emerging countries: Is there an augmented nonlinear taylor rule?," Economic Modelling, Elsevier, vol. 72(C), pages 306-319.
    2. Hinterlang, Natascha & Hollmayr, Josef, 2022. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Journal of Macroeconomics, Elsevier, vol. 74(C).
    3. Manuel Gonzalez-Astudillo & Jean-Philippe Laforte, 2020. "Estimates of r* Consistent with a Supply-Side Structure and a Monetary Policy Rule for the U.S. Economy," Finance and Economics Discussion Series 2020-085, Board of Governors of the Federal Reserve System (U.S.).
    4. Hinterlang, Natascha & Hollmayr, Josef, 2020. "Classification of monetary and fiscal dominance regimes using machine learning techniques," Discussion Papers 51/2020, Deutsche Bundesbank.
    5. Panovska, Irina & Ramamurthy, Srikanth, 2022. "Decomposing the output gap with inflation learning," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    6. Chang, Yoosoon & Kwak, Boreum & Qiu, Shi, 2021. "U.S. monetary and fiscal policy regime changes and their interactions," IWH Discussion Papers 12/2021, Halle Institute for Economic Research (IWH).
    7. Kliem, Martin & Kriwoluzky, Alexander & Sarferaz, Samad, 2016. "Monetary–fiscal policy interaction and fiscal inflation: A tale of three countries," European Economic Review, Elsevier, vol. 88(C), pages 158-184.
    8. Hinterlang, Natascha & Hollmayr, Josef, 2021. "Classification of monetary and fiscal dominance regimes using machine learning techniques," IMFS Working Paper Series 160, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    9. Manuel Gonzalez-Astudillo & Rakeen Tanvir, 2023. "Hawkish or Dovish Fed? Estimating a Time-Varying Reaction Function of the Federal Open Market Committee's Median Participant," Finance and Economics Discussion Series 2023-070, Board of Governors of the Federal Reserve System (U.S.).

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    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E63 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization; Treasury Policy

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