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With inflation remaining low for the sixth consecutive year, the Canadian economy recorded a strong expansion of about 4 per cent through 1997.

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

The COVID-19 pandemic has caused an atypical recession in which some sectors of the economy boomed and others collapsed. This required a unique fiscal policy reaction to both support firms and stimulate activity in sectors with slack. Was fiscal policy able to get where it was needed? Mostly, yes.

Growth in the global economy began to slow in the fourth quarter of 2007 and the first quarter of 2008. This reflected the effects of the slowdown in the U.S. economy and ongoing dislocations in global financial markets.

The Canadian economy continues to grow at a solid pace, consistent with the Bank’s outlook in the January Monetary Policy Report Update.

We study how the distribution of information supply by the news media affects the macroeconomy. We find that media coverage focuses particularly on the largest firms, and that firms’ equity financing and investment increase after media coverage. But these equity and investment responses are largest among small, rarely covered firms. Our quantitative studies highlight that the aggregate effects of media coverage depend crucially on how that coverage is allocated.

The Canadian economy continues to operate just above its full production capacity, and the near-term outlook for core inflation has moved slightly higher.

This equilibrium model explains the trend in long-term yields and business-cycle movements in short-term yields and yield spreads. The less-frequent inverted yield curves (and less-frequent recessions) after the 1990s are due to recent secular stagnation and procyclical inflation expectations.

Modeling and estimating persistent discrete data can be challenging. In this paper, we use an autoregressive panel probit model where the autocorrelation in the discrete variable is driven by the autocorrelation in the latent variable. In such a non-linear model, the autocorrelation in an unobserved variable results in an intractable likelihood containing high-dimensional integrals.

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.