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We propose a macroeconomic model with a nonlinear Phillips curve that has a flat slope when inflationary pressures are subdued and steepens when inflationary pressures are elevated. Our model can generate more sizable inflation surges due to cost-push and demand shocks than a standard linearized model when inflation is high.

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 examine the potential impacts of a severe economic shock on the resilience of major banks in Canada. We find these banks would suffer significant financial losses but nevertheless remain resilient. This underscores the role well-capitalized banks and sound underwriting practices play in supporting economic activity in a downturn.

We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges.

Did supply disruptions and cost pressures play a role in rising inflation in 2020 during the COVID-19 pandemic? Using data collected from websites of large retailers in multiple sectors and countries, we show that shortages may indicate transitory inflationary pressures.

We expect potential output growth to be higher than in the October 2020 reassessment. By 2024, growth will be slightly above its average growth from 2010 to 2019. We assess that the Canadian nominal neutral rate continues to lie in the range of 1.75 to 2.75 percent.

Models with default options are hard to solve. We propose an extension of the endogenous grid method that solves default risk models more efficiently and accurately.

Central banks conduct research involving in-depth interviews with external parties—but little is known about how this information affects monetary policy. We address this gap by analyzing open-ended interviews with senior central bank economic and policy staff who work closely with policy decision-makers.

We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.

After COVID-19, we expect potential output growth to stabilize around 1.2 percent. This is lower than the 2010–18 average growth of 1.8 percent. Relative to the April 2019 reassessment, the growth profile is revised down. Given the unknown course of the pandemic, uncertainty around these estimates is higher than in previous years.