Decision Synthesis in Monetary Policy

Tony Chernis, Gary Koop, Emily Tallman and Mike West

Staff Working Papers from Bank of Canada

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.

Keywords: Econometric and Statistical Methods; Economic Models; Monetary Policy (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2024-08
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Related works:
Working Paper: Decision synthesis in monetary policy (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:24-30

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