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The Bank of Canada’s current suite of models faces challenges in addressing network effects that integrate household and firm-level heterogeneity and their behaviours. We develop CANVAS, a Canadian behavioural agent-based model to contribute to the Bank’s next-generation modelling effort. CANVAS improves forecasting performance and expands capacity for model-based scenario analysis.

Business cycles are substantially correlated across countries. Yet most existing models are not able to generate substantial transmission through international trade. We show that the nature of such transmission depends fundamentally on the features determining the responsiveness of labor supply and labor demand to international relative prices.

We develop a model to analyze the link between financial leverage, worker pay structure and the risk of job termination. Contrary to the conventional view, we show that even in the absence of any agency problem among workers, variable pay can be optimal despite workers being risk averse and firms risk neutral.

I show how to combine large numbers of forecasts using several approaches within the framework of a Bayesian predictive synthesis. I find techniques that choose and combine a handful of forecasts, known as global-local shrinkage priors, perform best.

This paper studies the effects of financial development, taking into account both formal and informal financing. Using cross-country firm-level data, we document that informal financing is utilized more by rich countries than poor countries.

We study competition for consumer attention, in which platforms can sacrifice service quality for attention. A platform can choose the “addictiveness” of its service.

This paper presents updated estimates of potential output growth for the global economy through 2022. Global potential output growth is expected to decline sharply in the aftermath of the COVID-19 pandemic and recover partially by the end of the projection horizon of the October 2020 Monetary Policy Report.

The degree to which financial constraint is binding is often not directly observable in commonly used business data sets (e.g., Compustat). In this paper, we measure and estimate the likelihood of a firm being constrained by external financing using a data set of small- and medium-sized Canadian firms.

We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence — contemporaneously and with a lag — the dynamics of the intercept and autoregressive coefficients in these models.