Econometric and statistical methods - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-28T08:11:19+00:00Forecasting Recessions in Canada: An Autoregressive Probit Model Approach
https://www.bankofcanada.ca/2024/03/staff-working-paper-2024-10/
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.2024-03-27T12:42:07+00:00enForecasting Recessions in Canada: An Autoregressive Probit Model Approach2024-03-27Business fluctuations and cyclesEconometric and statistical methodsStaff Working Paper 2024-10https://www.bankofcanada.ca/wp-content/uploads/2024/03/swp2024-10.pdfForecasting Recessions in Canada: An Autoregressive Probit Model ApproachAntoine Poulin-MooreKerem TuzcuogluMarch 2024CC5C51C53EE3E32COVID-19 Hasn’t Killed Merchant Acceptance of Cash: Results from the 2023 Merchant Acceptance Survey
https://www.bankofcanada.ca/2024/03/staff-discussion-paper-2024-2/
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.2024-03-25T12:20:03+00:00enCOVID-19 Hasn’t Killed Merchant Acceptance of Cash: Results from the 2023 Merchant Acceptance Survey2024-03-25Bank notesDigital currencies and fintechEconometric and statistical methodsStaff Discussion Paper 2024-2https://www.bankofcanada.ca/wp-content/uploads/2024/03/sdp2024-2.pdfCOVID-19 Hasn’t Killed Merchant Acceptance of Cash: Results from the 2023 Merchant Acceptance SurveyAngelika WelteKatrina TalaveraLiang WangJoy WuMarch 2024CC8DD2D22EE4LL2Flood risk and residential lending
https://www.bankofcanada.ca/2024/01/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.2024-01-15T15:00:26+00:00enFlood risk and residential lending2024-01-15Predictive Density Combination Using a Tree-Based Synthesis Function
https://www.bankofcanada.ca/2023/12/staff-working-paper-2023-61/
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.2023-12-28T13:20:25+00:00enPredictive Density Combination Using a Tree-Based Synthesis Function2023-12-28Econometric and statistical methodsStaff Working Paper 2023-61https://www.bankofcanada.ca/wp-content/uploads/2023/12/swp2023-61.pdfPredictive Density Combination Using a Tree-Based Synthesis FunctionTony ChernisNiko HauzenbergerFlorian HuberGary KoopJames MitchellDecember 2023CC1C11C3C32C5C53Climate-Related Flood Risk to Residential Lending Portfolios in Canada
https://www.bankofcanada.ca/2023/12/staff-discussion-paper-2023-33/
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.2023-12-20T15:56:24+00:00enClimate-Related Flood Risk to Residential Lending Portfolios in Canada2023-12-20Central bank researchClimate changeCredit risk managementEconometric and statistical methodsFinancial institutionsFinancial stabilityStaff Discussion Paper 2023-33https://www.bankofcanada.ca/wp-content/uploads/2023/12/sdp2023-33.pdfClimate-Related Flood Risk to Residential Lending Portfolios in CanadaCraig JohnstonGeneviève ValléeHossein HosseiniBrett LindsayMiguel MolicoMarie-Christine TremblayAidan WittsDecember 2023CC8C81GG2G21QQ5Q54Finding the balance—measuring risks to inflation and to GDP growth
https://www.bankofcanada.ca/2023/12/staff-analytical-note-2023-18/
Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023.2023-12-19T11:22:43+00:00enFinding the balance—measuring risks to inflation and to GDP growth2023-12-19Testing Collusion and Cooperation in Binary Choice Games
https://www.bankofcanada.ca/2023/11/staff-working-paper-2023-58/
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.2023-11-27T11:56:12+00:00enTesting Collusion and Cooperation in Binary Choice Games2023-11-27Econometric and statistical methodsMarket structure and pricingStaff Working Paper 2023-58https://www.bankofcanada.ca/wp-content/uploads/2023/11/swp2023-58.pdfTesting Collusion and Cooperation in Binary Choice GamesErhao XieNovember 2023CC5C57LL1L13Machine learning for economics research: when, what and how
https://www.bankofcanada.ca/2023/10/staff-analytical-note-2023-16/
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.2023-10-27T12:00:04+00:00enMachine learning for economics research: when, what and how2023-10-27Identifying Nascent High-Growth Firms Using Machine Learning
https://www.bankofcanada.ca/2023/10/staff-working-paper-2023-53/
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.2023-10-16T15:24:11+00:00enIdentifying Nascent High-Growth Firms Using Machine Learning2023-10-16Econometric and statistical methodsFirm dynamicsStaff Working Paper 2023-53https://www.bankofcanada.ca/wp-content/uploads/2023/10/swp2023-53.pdfIdentifying Nascent High-Growth Firms Using Machine LearningStephanie HouleRyan MacdonaldOctober 2023CC5C55C8C81LL2L25Predicting Changes in Canadian Housing Markets with Machine Learning
https://www.bankofcanada.ca/2023/09/staff-discussion-paper-2023-21/
We apply two machine learning algorithms to forecast monthly growth of house prices and existing homes sales in Canada. Although the algorithms can sometimes outperform a linear model, the improvement in forecast accuracy is not always statistically significant.2023-09-29T13:04:48+00:00enPredicting Changes in Canadian Housing Markets with Machine Learning2023-09-29Econometric and statistical methodsFinancial marketsHousingStaff Discussion Paper 2023-21https://www.bankofcanada.ca/wp-content/uploads/2023/09/sdp2023-21.pdfStaff Discussion Paper 2023-21Johan BrannlundHelen LaoMaureen MacIsaacJing YangSeptember 2023ACC4C45C5C53DD2RR2R3