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We present a new corporate default model, one of the building blocks of the Bank of Canada’s bank stress-testing infrastructure. The model is used to forecast corporate loan losses of the Canadian banking sector under stress.

A low level of the neutral rate of interest increases the likelihood that a central bank’s policy rate will reach its effective lower bound (ELB) in future economic downturns. In a low neutral rate environment, using an extended monetary policy toolkit including forward guidance helps address the ELB challenge. Using the Bank’s Terms-of-Trade Economic Model, we assess the benefits and limitations of a state-contingent forward guidance implemented within a flexible inflation targeting framework.

We introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.

When countries are hit by supply shocks, central banks often face the dilemma of either looking through such shocks or reacting to them to ensure that inflation expectations remain anchored. In this paper, we propose a tractable framework to capture this dilemma and then explore optimal policy under a range of assumptions about how expectations are formed.

How do households respond when they receive unanticipated income, such as an inheritance or government stimulus cheque? This paper studies these windfall income shocks through a model of household behaviour that generates a realistic consumption response for households along the entire distribution of wealth.

Using a nonlinear New Keynesian model with a financial accelerator, we show that financial frictions generate large state-dependent amplification effects. Shocks propagate more strongly in periods of financial stress. We propose an endogenous regime-switching DSGE framework for efficient estimation and improved model fit.

How should central banks design monetary policy in stable times and during recessions? We run a horse race between five monetary policy frameworks in an experimental laboratory to assess how well the different approaches can manage the public’s expectations and stabilize the economy.

We summarize the theoretical model of central bank asset purchases developed in Cimon and Walton (2022). The model helps us understand how asset purchases ease pressures on investment dealers to restore market conditions in a crisis.

Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

We study economies where firms acquire capital in primary markets then retrade it in secondary markets after information on idiosyncratic productivity arrives. Our secondary markets incorporate bilateral trade with search, bargaining and liquidity frictions.