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Decision Synthesis in Monetary Policy

Author

Listed:
  • Tony Chernis
  • Gary Koop
  • Emily Tallman
  • Mike West

Abstract

The macroeconomy is a complicated dynamic system with significant uncertainties that make modelling difficult. Consequently, decision-makers consider multiple models that provide different predictions and policy recommendations and then synthesize that information into a policy decision. We use Bayesian predictive decision synthesis (BPDS) as a way formalize this monetary policy decision-making process. BPDS draws on recent developments in model combination and statistical decision theory that make it possible to combine models in a manner that incorporates decision goals, expectations and outcomes. We develop a BPDS procedure for a case study of monetary policy decision-making with an inflation-targeting central bank and compare the results against standard model-combination approaches.

Suggested Citation

  • Tony Chernis & Gary Koop & Emily Tallman & Mike West, 2024. "Decision Synthesis in Monetary Policy," Staff Working Papers 24-30, Bank of Canada.
    • Handle: RePEc:bca:bocawp:24-30
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    References listed on IDEAS

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    More about this item

    Keywords

    Econometric and Statistical Methods; Economic Models; Monetary Policy;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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