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

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.