Non-Parametric Identification and Testing of Quantal Response Equilibrium Staff working paper 2024-24 Johannes Hoelzemann, Ryan Webb, Erhao Xie We show that the utility function and the error distribution are non-parametrically over-identified under Quantal Response Equilibrium (QRE). This leads to a simple test for QRE. We illustrate our method in a Monte Carlo exercise and a laboratory experiment. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C14, C5, C57, C9, C92 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models
Decomposing Systemic Risk: The Roles of Contagion and Common Exposures Staff working paper 2024-19 Grzegorz Halaj, Ruben Hipp We examine systemic risks within the Canadian banking sector, decomposing them into three contribution channels: contagion, common exposures, and idiosyncratic risk. Through a structural model, we dissect how interbank relationships and market conditions contribute to systemic risk, providing new insights for financial stability. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C3, C32, C5, C51, G, G2, G21, L, L1, L14 Research Theme(s): Financial system, Financial stability and systemic risk, Models and tools, Econometric, statistical and computational methods, Economic models
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
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
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 JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17 Research Theme(s): Models and tools, Economic models, Monetary policy, Inflation dynamics and pressures, Monetary policy framework and transmission, Real economy and forecasting
Making It Real: Bringing Research Models into Central Bank Projections Staff discussion paper 2023-29 Marc-André Gosselin, Sharon Kozicki Macroeconomic projections and risk analyses play an important role in guiding monetary policy decisions. Models are integral to this process. This paper discusses how the Bank of Canada brings research models and lessons learned from those models into the central bank projection environment. Content Type(s): Staff research, Staff discussion papers JEL Code(s): C, C3, C32, C5, C51, E, E3, E37, E4, E47, E5, E52 Research Theme(s): Models and tools, Economic models, Monetary policy, Monetary policy framework and transmission, 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