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Two major issues dominate the analysis and policy discussion in this Monetary Policy Report: the nature and extent of the global economic slowdown that began late last year and the consequences of the terrorist attacks in the United States.

The five articles in this issue present research and analysis by Bank staff covering a variety of topics: the growth of Canadian-dollar-denominated assets in official foreign reserves; the emergence of platform-based digital currencies; methods of forecasting the real price of oil; measures of uncertainty in monetary policy; and the recent performance of the labour market in Canada and the United States.

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.

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 propose an open-economy New Keynesian model with financial integration that allows financial intermediaries to hold foreign long-term bonds. We study the implications of financial integration on monetary policy transmission. Among various aspects of financial integration, the bond duration plays a major role. These results hold for conventional and unconventional monetary policies.

We analyze 40 years’ worth of natural disasters using a local projection framework to assess their impact on provincial labour markets in Canada. We find that disasters decrease hours worked within a week and lower wage growth in the medium run. Our study highlights that disasters affect vulnerable workers through the income channel.

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.

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.