Year: 2024
Type: preprint
Abstract: The macroeconomy is a sophisticated dynamic system involving significant uncertainties that complicate modelling. In response, decision-makers consider multiple models that provide different predictions and policy recommendations which are then synthesized into a policy decision. In this setting, we develop Bayesian predictive decision synthesis (BPDS) to formalize monetary policy decision processes. BPDS draws on recent developments in model combination and statistical decision theory that yield new opportunities in combining multiple models, emphasizing the integration of decision goals, expectations and outcomes into the model synthesis process. Our case study concerns central bank policy decisions about target interest rates with a focus on implications for multi-step macroeconomic forecasting. This application also motivates new methodological developments in conditional forecasting and BPDS, presented and developed here.
DOI: 10.34989/swp-2024-30
Authors: Chernis, Tony, Koop, Gary, Tallman, Emily, West, Mike
Institutions: Bank of Canada, University of Strathclyde, Duke University
Language: English
Domain: Social Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W6965499887
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