Assessing global potential output growth and the US neutral rate: April 2021 Staff analytical note 2021-5 Thomas J. Carter, Xin Scott Chen, Ali Jaffery, Christopher Hajzler, Jonathan Lachaine, Peter Shannon, Subrata Sarker, Graeme Westwood, Beiling Yan We expect global potential output growth to rise to 3 percent by 2022. Relative to the last assessment in October 2020, potential output growth has been revised up across all the regions. The range of the US neutral rate remains unchanged relative to the autumn 2020 assessment. Content Type(s): Staff research, Staff analytical notes JEL Code(s): E, E1, E2, E4, E5, F, F0, O, O4 Research Theme(s): Monetary policy, Monetary policy framework and transmission, Real economy and forecasting, Structural challenges, Demographics and labour supply
Estimating Policy Functions in Payments Systems Using Reinforcement Learning Staff working paper 2021-7 Pablo S. Castro, Ajit Desai, Han Du, Rodney J. Garratt, Francisco Rivadeneyra We demonstrate the ability of reinforcement learning techniques to estimate the best-response functions of banks participating in high-value payments systems—a real-world strategic game of incomplete information. Content Type(s): Staff research, Staff working papers JEL Code(s): A, A1, A12, C, C7, D, D8, D83, E, E4, E42, E5, E58 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Digital assets and fintech, Payment and financial market infrastructures
2017 Methods-of-Payment Survey Report Staff discussion paper 2018-17 Christopher Henry, Kim Huynh, Angelika Welte Cash use is declining while contactless and mobile payments are on the rise. Content Type(s): Staff research, Staff discussion papers JEL Code(s): D, D8, D83, E, E4, E41 Research Theme(s): Money and payments, Cash and bank notes, Payment and financial market infrastructures, Retail payments
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
Partial Identification of Heteroskedastic Structural Vector Autoregressions: Theory and Bayesian Inference Staff working paper 2025-14 Helmut Lütkepohl, Fei Shang, Luis Uzeda, Tomasz Woźniak We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C11, C12, C3, C32, E, E6, E62 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Real economy and forecasting
Survival Analysis of Bank Note Circulation: Fitness, Network Structure and Machine Learning Staff working paper 2020-33 Diego Rojas, Juan Estrada, Kim Huynh, David T. Jacho-Chávez Using the Bank of Canada's Currency Information Management Strategy, we analyze the network structure traced by a bank note’s travel in circulation and find that the denomination of the bank note is important in our potential understanding of the demand and use of cash. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C5, C52, C6, C65, C8, C81, E, E4, E42, E5, E51 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Cash and bank notes
The Role of the International Monetary Fund in the Post-Crisis World Staff discussion paper 2016-6 Mark Kruger, Robert Lavigne, Julie McKay The International Monetary Fund (IMF, or the Fund) has undergone a number of significant policy changes and reforms in the wake of the global financial crisis. Most notably, in December 2015, the United States approved long-delayed legislation to increase the representation of developing countries in the Fund’s governance structure. Content Type(s): Staff research, Staff discussion papers JEL Code(s): F, F3, F33 Research Theme(s): Financial markets and funds management, International markets and currencies, Financial system, Financial stability and systemic risk, Financial system regulation and oversight, Structural challenges, International trade, finance and competitiveness
A Generalized Endogenous Grid Method for Default Risk Models Staff working paper 2021-11 Youngsoo Jang, Soyoung Lee Models with default options are hard to solve. We propose an extension of the endogenous grid method that solves default risk models more efficiently and accurately. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C6, C63, E, E3, E37 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models
The Countercyclical Capital Buffer and International Bank Lending: Evidence from Canada Staff working paper 2021-61 David Xiao Chen, Christian Friedrich We examine the impact of the CCyB on foreign lending activities of Canadian banks. We show that the announcement of a tightening in another country’s CCyB leads to a decrease in the growth rate of cross-border lending between Canadian banks and borrowers in that other country. Content Type(s): Staff research, Staff working papers JEL Code(s): E, E3, E32, F, F2, F21, F3, F32, G, G2, G21, G28 Research Theme(s): Financial markets and funds management, International markets and currencies, Financial system, Financial stability and systemic risk, Financial system regulation and oversight
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