Finding a Needle in a Haystack: A Machine Learning Framework for Anomaly Detection in Payment Systems Staff working paper 2024-15 Ajit Desai, Anneke Kosse, Jacob Sharples Our layered machine learning framework can enhance real-time transaction monitoring in high-value payment systems, which are a central piece of a country’s financial infrastructure. When tested on data from Canadian payment systems, it demonstrated potential for accurately identifying anomalous transactions. This framework could help improve cyber and operational resilience of payment systems. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C4, C45, C5, C55, D, D8, D83, E, E4, E42 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Payment and financial market infrastructures
Parallel Tempering for DSGE Estimation Staff working paper 2024-13 Joshua Brault I develop a population-based Markov chain Monte Carlo algorithm known as parallel tempering to estimate dynamic stochastic general equilibrium models. Parallel tempering approximates the posterior distribution of interest using a family of Markov chains with tempered posteriors. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C11, C15, E, E1, E10 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models
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 JEL Code(s): E, E4, E43, E44, E47, G, G1, G14 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Monetary policy framework and transmission, Structural challenges, International trade, finance and competitiveness
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 JEL Code(s): C, C5, C51, C53, E, E3, E32 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
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 JEL Code(s): C, C1, C11, C3, C32, C5, C53 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
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 JEL Code(s): C, C5, C57, L, L1, L13 Research Theme(s): Financial markets and funds management, Market structure, Models and tools, Econometric, statistical and computational methods
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 JEL Code(s): A, A1, A10, B, B2, B23, C, C4, C45, C5, C55 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity
Identifying Nascent High-Growth Firms Using Machine Learning Staff working paper 2023-53 Stéphanie 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 JEL Code(s): C, C5, C55, C8, C81, L, L2, L25 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity
Predicting Changes in Canadian Housing Markets with Machine Learning Staff discussion paper 2023-21 Johan Brannlund, Helen Lao, Maureen MacIsaac, Jing Yang 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. Content Type(s): Staff research, Staff discussion papers JEL Code(s): A, C, C4, C45, C5, C53, D, D2, R, R2, R3 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
Forecasting Risks to the Canadian Economic Outlook at a Daily Frequency Staff discussion paper 2023-19 Chinara Azizova, Bruno Feunou, James Kyeong This paper quantifies tail risks in the outlooks for Canadian inflation and real GDP growth by estimating their conditional distributions at a daily frequency. We show that the tail risk probabilities derived from the conditional distributions accurately reflect realized outcomes during the sample period from 2002 to 2022. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting