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Dynamic general-equilibrium models (DGEMs) are being increasingly used in macroeconomic research. In this article, the author describes the main features of these models and outlines their contribution to economic research performed at the Bank of Canada. He notes that the basic principle of DGEMs is that the modelling of economic activity, even on a scale as large as the economy of a country, should start with a series of microeconomic problems (at the scale of individuals), which, once resolved, are aggregated to represent the macroeconomic reality described by the model.

We study how participants in the Lynx payment system use the net send limit (NSL) tool to control their intraday payment outflow levels. Our results show that participants typically adopt a “set it and forget it” approach to scheduling NSLs and sometimes have distinct intraday NSL adjustment behaviours.

This paper provides an extensive evaluation of the performance of quantile vector autoregression (QVAR) to forecast macroeconomic risk. Generally, QVAR outperforms standard benchmark models. Moreover, QVAR and QVAR augmented with factors perform equally well. Both are adequate for modeling macroeconomic risks.

In the interbank market, banks will sometimes trade below the central bank's deposit rate. We explain this anomaly using a theory based on market frictions and relationship lending.

We investigate competition between two intrinsically worthless currencies as a result of decentralized interactions between human subjects. We design a laboratory experiment based on a simple two-country, two-currency search model to study factors that affect circulation patterns and equilibrium selection.

We develop an algorithm and run it on a hybrid quantum annealing solver to find an ordering of payments that reduces the amount of system liquidity necessary without substantially increasing payment delays.

This paper proposes a method for estimating the joint distribution of two or more variables when only their marginal distributions and the distribution of their aggregates are observed. Nonparametric identification is achieved by modelling dependence using a latent common-factor structure.

We review the literature on central bank forecasting with a special focus on the Federal Reserve, European Central Bank, Bank of England and Bank of Canada.

We measure systemic risk in the network of financial market infrastructures (FMIs) as the probability that two or more FMIs have a large credit risk exposure to the same FMI participant.

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.