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Estimating Central Bank Preferences under Commitment and Discretion

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  • Gregory Erin Givens

Abstract

This paper explains US 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. I estimate the model 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.

Suggested Citation

  • Gregory Erin Givens, 2009. "Estimating Central Bank Preferences under Commitment and Discretion," Working Papers 200905, Middle Tennessee State University, Department of Economics and Finance.
  • Handle: RePEc:mts:wpaper:200905
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    More about this item

    Keywords

    Optimal Monetary Policy; Commitment; Discretion; Policy Preferences;
    All these keywords.

    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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