CANVAS: A Canadian behavioral agent-based model for monetary policy

Cars Hommes, Mario He, Sebastian Poledna, Melissa Siqueira and Yang Zhang

Journal of Economic Dynamics and Control, 2025, vol. 172, issue C

Abstract: We develop the Canadian behavioral Agent-Based Model (CANVAS) that complements traditional macroeconomic models for forecasting and monetary policy analysis. CANVAS represents a next-generation modeling effort featuring enhancements in three dimensions: introducing household and firm heterogeneity, departing from rational expectations, and modeling price and quantity setting heuristics within a production network. The expanded modeling capacity is achieved by harnessing large-scale Canadian micro- and macroeconomic datasets and incorporating adaptive learning and simple heuristics. The out-of-sample forecasting performance of CANVAS is found to be competitive with a benchmark vector auto-regressive (VAR) model and a DSGE model. When applied to analyze the COVID-19 pandemic episode, our model helps explain both the macroeconomic movement and the interplay between expectation formation and cost-push shocks. CANVAS is one of the first macroeconomic agent-based models applied by a central bank to support projection and alternative scenarios, marking an advancement in the toolkit of central banks and enriching monetary policy analysis.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:172:y:2025:i:c:s0165188924001787

DOI: 10.1016/j.jedc.2024.104986

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