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The Bitcoin Omnibus Survey is an important tool for monitoring Canadians’ awareness and ownership of bitcoin and other cryptoassets over time. In this paper, we present data highlights from the 2023 survey.

We quantify the reaction of U.S. equity, bond futures, and exchange rate returns to oil price shocks driven by oil inventory news.

We identify and quantify fiscal news shocks, compiling events (2001–2023) that altered the expected path of U.S. defense expenditure. For each event, we estimate market-implied shifts in expected spending. A shift-share analysis yields a two-year, metropolitan statistical area–level GDP multiplier of approximately 1 for U.S. military build-ups.

We develop an algorithm and run it on a hybrid quantum annealing solver to find an ordering of payments that reduces the amount of system liquidity necessary without substantially increasing payment delays.

A nation's productivity is the prime determinant of its real incomes and standard of living, as well as being a major determinant of its potential output. In the short run, deviations of actual output from potential output are a useful indicator of inflationary pressures. This article is a short summary of the proceedings of the workshop, which focus on productivity and potential output growth among industrialized countries. The research is organized under three main themes: estimating potential growth; productivity and growth; and institutions, policies, and growth.

This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used.

We propose a macroeconomic model in which adverse selection in investment drives the amplification of macroeconomic fluctuations, in line with prominent roles played by the credit crunch and collapse of the asset-backed security market in the financial crisis.

Since the May Report, the international economic environment has continued to improve. Economic activity abroad grew faster than expected, while inflation in the major economies remained subdued.

I develop a method for Bayesian estimation of globally solved, non-linear macroeconomic models. The method uses a mixture density network to approximate the initial state distribution. The mixture density network results in more reliable posterior inference compared with the case when the initial states are set to their steady-state values.

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes.