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Most models in finance assume that agents make trading plans over the infinite future. We consider instead that they are boundedly rational and may only form forecasts over a limited horizon.

Carbon dioxide emissions have been commonly modelled as rising and falling with total output. Yet many factors, such as energy-efficiency improvements and shifts to cleaner energy, can break this relationship. We evaluate these factors using US data and find that changes in energy efficiency of consumption goods explain a significant proportion of emissions fluctuations. This finding also implies that models that omit energy efficiency likely overestimate the trade-off between environmental protection and economic performance.

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.

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.

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.

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 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.

In the year just ended, the global economy faced a number of exceptional challenges, reflecting a wide range of economic, financial, and geopolitical risks and uncertainties. These included the fallout from the September 2001 terrorist attacks, corporate accounting scandals, stock market volatility, and developments in the Middle East. Despite this global backdrop, the Canadian economy outperformed virtually all other industrial economies, growing by about 3 1/4 per cent and creating 560,000 jobs, while inflation expectations remained well anchored to the Bank of Canada’s 2 per cent inflation-control target.

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.

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.