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2121 Results

Machine learning for economics research: when, what and how

Staff analytical note 2023-16 Ajit Desai
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.
August 16, 2012

Bank of Canada Review - Summer 2012

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.

Canadians’ access to cash in 2023

Staff analytical note 2025-13 Heng Chen, Hongyu Xiao, Daneal O’Habib, Stephen Wild
This study updates our measure of Canadians' access to cash through automated banking machines and financial institution branches. We find that in 2023 overall access to cash remains stable, while rural Canadians continue having less access.
Content Type(s): Staff research, Staff analytical notes JEL Code(s): J, J1, J15, O, O1, R, R5, R51 Research Theme(s): Money and payments, Cash and bank notes

Let’s Get Physical: Impacts of Climate Change Physical Risks on Provincial Employment

Staff working paper 2024-32 Thibaut Duprey, Soojin Jo, Geneviève Vallée
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.

The Dynamics of Capital Flow Episodes

Staff working paper 2016-9 Christian Friedrich, Pierre Guérin
This paper proposes a novel methodology for identifying episodes of strong capital flows based on a regime-switching model. In comparison with the existing literature, a key advantage of our methodology is to estimate capital flow regimes without the need for context- and sample-specific assumptions.
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