Search

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 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.

I study a model of competing data intermediaries (e.g., online platforms and data brokers) that collect personal data from consumers and sell it to downstream firms.

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 study how participants in the Lynx payment system use the net send limit (NSL) tool to control their intraday payment outflow levels. Our results show that participants typically adopt a “set it and forget it” approach to scheduling NSLs and sometimes have distinct intraday NSL adjustment behaviours.

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.

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.

This issue features three articles that present research and analysis by Bank of Canada staff. The first updates previous Bank estimates of measurement bias in the Canadian consumer price index; the second uses a new term-structure model to analyze the relationship between the short-term policy rate and long-term interest rates; and the third examines indicators of balance-sheet risks at financial institutions in Canada.

The uncertainty regarding inflation that is observed in density forecasts of households and professionals helps macroeconomists understand the formation mechanism of inflation expectations. Shocks to inflation take time to be perceived by all agents in the economy, and such rigidity is lower in a high-inflation environment.