U.S. Macroeconomic News and Low-Frequency Changes in Small Open Economies’ Bond Yields Staff Working Paper 2024-12 Bingxin Ann Xing, Bruno Feunou, Morvan Nongni-Donfack, Rodrigo Sekkel Using two complementary approaches, we investigate the importance of U.S. macroeconomic news in driving low-frequency fluctuations in the term structure of interest rates in Canada, Sweden and the United Kingdom. We find that U.S. macroeconomic news is particularly important to explain changes in the expectation components of the nominal, real and break-even inflation rates of small open economies. Content Type(s): Staff research, Staff working papers Topic(s): Central bank research, Econometric and statistical methods JEL Code(s): E, E4, E43, E44, E47, G, G1, G14
Forecasting Recessions in Canada: An Autoregressive Probit Model Approach Staff Working Paper 2024-10 Antoine Poulin-Moore, Kerem Tuzcuoglu We forecast recessions in Canada using an autoregressive (AR) probit model. The results highlight the short-term predictive power of the US economic activity and suggest that financial indicators are reliable predictors of Canadian recessions. In addition, the suggested model meaningfully improves the ability to forecast Canadian recessions, relative to a variety of probit models proposed in the Canadian literature. Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C5, C51, C53, E, E3, E32
COVID-19 Hasn’t Killed Merchant Acceptance of Cash: Results from the 2023 Merchant Acceptance Survey Staff Discussion Paper 2024-2 Angelika Welte, Katrina Talavera, Liang Wang, Joy Wu The Bank of Canada’s Merchant Acceptance Survey finds that 96% of small and medium-sized businesses in Canada accepted cash in 2023. Acceptance of debit and credit cards has increased to 89%, and acceptance of digital payments has also increased. However, Canada is far from being a cashless society. Content Type(s): Staff research, Staff discussion papers Topic(s): Bank notes, Digital currencies and fintech, Econometric and statistical methods JEL Code(s): C, C8, D, D2, D22, E, E4, L, L2
January 15, 2024 Flood risk and residential lending Craig Johnston, Geneviève Vallée, Hossein Hosseini, Miguel Molico, Marie-Christine Tremblay, Aidan Witts We present key findings of a recent study that evaluates the credit risk that flooding poses to the residential lending activities of Canadian banks and credit unions. Results show that such risk currently appears modest but could become larger with climate change. Content Type(s): Publications, Financial System Hub articles Topic(s): Central bank research, Climate change, Credit risk management, Econometric and statistical methods, Financial institutions, Financial stability JEL Code(s): C, C8, C81, G, G2, G21, Q, Q5, Q54
Predictive Density Combination Using a Tree-Based Synthesis Function Staff Working Paper 2023-61 Tony Chernis, Niko Hauzenberger, Florian Huber, Gary Koop, James Mitchell This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C3, C32, C5, C53
Climate-Related Flood Risk to Residential Lending Portfolios in Canada Staff Discussion Paper 2023-33 Craig Johnston, Geneviève Vallée, Hossein Hosseini, Brett Lindsay, Miguel Molico, Marie-Christine Tremblay, Aidan Witts We assess the potential financial risks of current and projected flooding caused by extreme weather events in Canada. We focus on the residential real estate secured lending (RESL) portfolios of Canadian financial institutions (FIs) because RESL portfolios are an important component of FIs’ balance sheets and because the assets used to secure such loans are immobile and susceptible to climate-related extreme weather events. Content Type(s): Staff research, Staff discussion papers Topic(s): Central bank research, Climate change, Credit risk management, Econometric and statistical methods, Financial institutions, Financial stability JEL Code(s): C, C8, C81, G, G2, G21, Q, Q5, Q54
Finding the balance—measuring risks to inflation and to GDP growth Staff Analytical Note 2023-18 Bruno Feunou, James Kyeong Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023. Content Type(s): Staff research, Staff analytical notes Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17
Testing Collusion and Cooperation in Binary Choice Games Staff Working Paper 2023-58 Erhao Xie This paper studies the testable implication of players’ collusive or cooperative behaviour in a binary choice game with complete information. I illustrate the implementation of this test by revisiting the entry game between Walmart and Kmart. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Market structure and pricing JEL Code(s): C, C5, C57, L, L1, L13
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. Content Type(s): Staff research, Staff analytical notes Topic(s): Central bank research, Econometric and statistical methods, Economic models JEL Code(s): A, A1, A10, B, B2, B23, C, C4, C45, C5, C55
Identifying Nascent High-Growth Firms Using Machine Learning Staff Working Paper 2023-53 Stephanie Houle, Ryan Macdonald Firms that grow rapidly have the potential to usher in new innovations, products or processes (Kogan et al. 2017), become superstar firms (Haltiwanger et al. 2013) and impact the aggregate labour share (Autor et al. 2020; De Loecker et al. 2020). We explore the use of supervised machine learning techniques to identify a population of nascent high-growth firms using Canadian administrative firm-level data. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Firm dynamics JEL Code(s): C, C5, C55, C8, C81, L, L2, L25