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Constrained Discretion and Central Bank Transparency

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  • Francesco Bianchi

    () (Department of Economics, Duke University)

  • Leonardo Melosi

    () (Federal Reserve Bank of Chicago)

Abstract

We develop a theoretical framework to quantitatively assess the general equilibrium effects and welfare implications of central bank reputation and transparency. Monetary policy alternates between periods of active inflation stabilization and periods during which the emphasis on inflation stabilization is reduced. When the central bank only engages in short deviations from active monetary policy, inflation expectations remain anchored and the model captures the monetary approach described as constrained discretion. However, if the central bank deviates for a prolonged period of time, agents gradually become pessimistic about future monetary policy, the disanchoring of inflation expectations occurs, and uncertainty rises. Reputation determines the speed with which agents’' pessimism accelerates once the central bank starts deviating. When the model is fitted to U.S. data, we find that the Federal Reserve can accommodate contractionary technology shocks for up to five years before inflation expectations take off. Increasing transparency would improve welfare by anchoring agents’ expectations. Gains from transparency are even more sizeable for countries whose central banks have weak reputation

Suggested Citation

  • Francesco Bianchi & Leonardo Melosi, 2012. "Constrained Discretion and Central Bank Transparency," PIER Working Paper Archive 13-041, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Handle: RePEc:pen:papers:13-041
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    Cited by:

    1. Francesco Bianchi & Cosmin Ilut, 2017. "Monetary/Fiscal Policy Mix and Agent's Beliefs," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 26, pages 113-139, October.
    2. Philippe Andrade & Gaetano Gaballo & Eric Mengus & Benoît Mojon, 2019. "Forward Guidance and Heterogeneous Beliefs," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(3), pages 1-29, July.
    3. Francesco Bianchi & Leonardo Melosi, 2016. "Modeling The Evolution Of Expectations And Uncertainty In General Equilibrium," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57, pages 717-756, May.
    4. Istrefi, Klodiana & Mouabbi, Sarah, 2018. "Subjective interest rate uncertainty and the macroeconomy: A cross-country analysis," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 296-313.
    5. Leonardo Melosi, 2014. "Signaling Effects of Monteray Policy," 2014 Meeting Papers 830, Society for Economic Dynamics.
    6. Michael Ehrmann, 2015. "Targeting Inflation from Below: How Do Inflation Expectations Behave?," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 213-249, September.
    7. Stephen Hansen & Michael McMahon, 2016. "First Impressions Matter: Signalling as a Source of Policy Dynamics," Review of Economic Studies, Oxford University Press, vol. 83(4), pages 1645-1672.
    8. Leonardo Melosi, 2017. "Signalling Effects of Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 84(2), pages 853-884.
    9. Pablo Garcia, 2021. "Learning, expectations and monetary policy," BCL working papers 153, Central Bank of Luxembourg.
    10. Ehrmann, Michael & Gaballo, Gaetano & Hoffmann, Peter & Strasser, Georg, 2019. "Can more public information raise uncertainty? The international evidence on forward guidance," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 93-112.
    11. Ippei Fujiwara & Yuichiro Waki, 2015. "Private news and monetary policy forward guidance or (the expected virtue of ignorance)," Globalization Institute Working Papers 238, Federal Reserve Bank of Dallas.
    12. Francesco Bianchi & Leonardo Melosi, 2014. "Dormant Shocks and Fiscal Virtue," NBER Macroeconomics Annual, University of Chicago Press, vol. 28(1), pages 1-46.
    13. Xin Wei, 2020. "Dynamic Expectations Formation and U.S. Monetary Policy Regime Change," CAEPR Working Papers 2020-007, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    14. Levieuge, Grégory & Lucotte, Yannick & Pradines-Jobet, Florian, 2021. "The cost of banking crises: Does the policy framework matter?," Journal of International Money and Finance, Elsevier, vol. 110(C).
    15. Christian Matthes, 2015. "Figuring Out the Fed—Beliefs about Policymakers and Gains from Transparency," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(1), pages 1-29, February.
    16. McClung, Nigel, 2020. "E-stability vis-à-vis determinacy in regime-switching models," Journal of Economic Dynamics and Control, Elsevier, vol. 121(C).
    17. K. Istrefi & A. Piloiu, 2016. "Economic policy uncertainty and inflation expectations," Rue de la Banque, Banque de France, issue 33, november..
    18. Bianchi, Francesco, 2016. "Methods for measuring expectations and uncertainty in Markov-switching models," Journal of Econometrics, Elsevier, vol. 190(1), pages 79-99.
    19. Donato Masciandaro & Davide Romelli & Gaia Rubera, 2020. "Tweeting on Monetary Policy and Market Sentiments: The Central Bank Surprise Index," BAFFI CAREFIN Working Papers 20134, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

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    More about this item

    Keywords

    Bayesian learning; reputation; uncertainty; inflation expectations; Markov-switching models; impulse response;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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