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Estimating Central Bank Preferences under Commitment and Discretion

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  • GREGORY E. GIVENS

Abstract

This paper explains U.S. macroeconomic outcomes with an empirical New Keynesian model in which monetary policy minimizes the central bank’s loss function. The presence of expectations in the model forms a well‐known distinction between two modes of optimization, termed commitment and discretion. The model is estimated separately under each policy using maximum likelihood over the Volcker–Greenspan–Bernanke period. Comparisons of fit reveal that the data favor the specification with discretionary policy. Estimates of the loss function weights point to an excessive concern for interest rate smoothing in the commitment model but a more balanced concern relative to inflation and output stability in the discretionary model.
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Suggested Citation

  • Gregory E. Givens, 2012. "Estimating Central Bank Preferences under Commitment and Discretion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(6), pages 1033-1061, September.
    • Handle: RePEc:mcb:jmoncb:v:44:y:2012:i:6:p:1033-1061
    DOI: j.1538-4616.2012.00522.x
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    JEL classification:

    • 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
    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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