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Central bank accountability under adaptive learning

Author

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  • Marine Charlotte André
  • Meixing Dai

Abstract

Using a New Keynesian model, we examine the accountability issue in a delegation framework where private agents form expectations through adaptive learning while the central bank is rational and optimally sets monetary policy under discretion. Learning gives rise to an incentive for the central bank to accommodate less the effect of inflation expectations and cost-push shocks on inflation and induces thus a deviation of endogenous variables from rational expectations equilibrium. To help the central bank to better manage the intratemporal tradeoff, the government should nominate a liberal central banker, i.e., set a negative optimal inflation penalty according to the value of learning coefficient. By reducing the deviation of the feedback effects of inflation expectations and cost-push shocks on inflation and the output gap from the corresponding ones under rational expectations, the optimal inflation penalty allows the economy to be more efficient and improves the social welfare. The main conclusions are valid under both constant- and decreasing-gain learning.

Suggested Citation

  • Marine Charlotte André & Meixing Dai, 2015. "Central bank accountability under adaptive learning," Working Papers of BETA 2015-32, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
  • Handle: RePEc:ulp:sbbeta:2015-32
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    Keywords

    Adaptive learning; optimal monetary policy; accountability; inflation penalty; rational expectations.;

    JEL classification:

    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • 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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