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
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
Dynamic Consumer Cash Inventory Model Staff working paper 2025-22 Kim Huynh, Oleksandr Shcherbakov, André Stenzel We study consumer cash inventory behavior by developing a dynamic model of forward-looking consumers and estimating structural parameters of the model using detailed consumer survey data. Consumers facing holding and withdrawal costs solve a discrete-time continuous-control dynamic programming problem to optimally use cash at the point of sale. Content Type(s): Staff research, Staff working papers JEL Code(s): D, D1, D12, D14, E, E4, E41, E42, G, G2, G21 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Money and payments, Cash and bank notes
Detecting Scapegoat Effects in the Relationship Between Exchange Rates and Macroeconomic Fundamentals Staff working paper 2017-22 Lorenzo Pozzi, Barbara Sadaba This paper presents a new testing method for the scapegoat model of exchange rates that aims to tighten the link between the theory on scapegoats and its empirical implementation. This new testing method consists of a number of steps. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C3, C32, F, F3, F31, G, G1, G15 Research Theme(s): Financial markets and funds management, International markets and currencies, Models and tools, Econometric, statistical and computational methods
The Central Bank’s Dilemma: Look Through Supply Shocks or Control Inflation Expectations? Staff working paper 2022-41 Paul Beaudry, Thomas J. Carter, Amartya Lahiri When countries are hit by supply shocks, central banks often face the dilemma of either looking through such shocks or reacting to them to ensure that inflation expectations remain anchored. In this paper, we propose a tractable framework to capture this dilemma and then explore optimal policy under a range of assumptions about how expectations are formed. Content Type(s): Staff research, Staff working papers JEL Code(s): E, E1, E12, E2, E24, E3, E31, E5, E52, E58, E6, E65 Research Theme(s): Models and tools, Economic models, Monetary policy, Inflation dynamics and pressures, Monetary policy framework and transmission
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
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
How Banks Create Gridlock to Save Liquidity in Canada's Large Value Payment System Staff working paper 2023-26 Rodney J. Garratt, Zhentong Lu, Phoebe Tian We show how participants in Canada’s new high-value payment system save liquidity by exploiting the new gridlock resolution arrangement. The findings have important implications for the design of these systems and shed light on financial institutions’ liquidity preference. Content Type(s): Staff research, Staff working papers JEL Code(s): E, E4, E42, E5, E58, G, G2, G21 Research Theme(s): Financial markets and funds management, Market functioning, Money and payments, Payment and financial market infrastructures
The Mutable Geography of Firms’ International Trade Staff working paper 2025-11 Lu Han Exporters frequently change their market destinations. This paper introduces a new approach to identifying the drivers of these decisions over time. Analysis of customs data from China and the UK shows most changes are driven by demand rather than supply-related shocks. Content Type(s): Staff research, Staff working papers JEL Code(s): F, F1, F12, F14, L, L1, L11 Research Theme(s): Models and tools, Economic models, Structural challenges, International trade, finance and competitiveness
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