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In our analysis of the US productivity slowdown in the 1970s and 2000s, we find that a significant portion of this deceleration can be attributed to a lack of improvement in allocative efficiency across sectors. Our analysis further identifies increased sector-level volatility as a major contributor to this lack of improvement in allocative efficiency.

The global economy has continued to grow at a robust pace since the July Monetary Policy Report Update.

The year that just passed posed many challenges for all Canadians. The slowdown in the global economy became more pronounced as the year went on, and this affected households, businesses, and governments alike. The tragedy of 11 September compounded the economic difficulties and issues facing us all. Through this period of rapidly changing circumstances, the Bank met its responsibilities by responding quickly and vigorously to events in order to underpin confidence and support the economy.

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.

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

Growth in the Canadian economy has been essentially in line with the expectations set out in the Bank’s January Monetary Policy Report Update.

The Canadian economy continued to expand robustly in 2000 while inflation remained low.

The Canadian economy continues to adjust to developments in the global economy.

This paper relaxes the Bayesian Nash equilibrium (BNE) assumption commonly imposed in empirical discrete choice games with incomplete information. Instead of assuming that players have unbiased/correct expectations, my model treats a player’s belief about the behavior of other players as an unrestricted unknown function. I study the joint identification of belief and payoff functions.

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.