Econometric and statistical methods
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Forecasting Recessions in Canada: An Autoregressive Probit Model Approach
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. -
COVID-19 Hasn’t Killed Merchant Acceptance of Cash: Results from the 2023 Merchant Acceptance Survey
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. -
January 15, 2024
Flood risk and residential lending
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. -
Predictive Density Combination Using a Tree-Based Synthesis Function
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. -
Climate-Related Flood Risk to Residential Lending Portfolios in Canada
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. -
Finding the balance—measuring risks to inflation and to GDP growth
Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023. -
Testing Collusion and Cooperation in Binary Choice Games
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. -
Machine learning for economics research: when, what and how
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. -
Identifying Nascent High-Growth Firms Using Machine Learning
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.