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
Price Discounts and Cheapflation During the Post-Pandemic Inflation Surge Staff working paper 2024-31 Alberto Cavallo, Oleksiy Kryvtsov We study how price variation within a store changes with inflation, and whether households exploit these changes to reduce the burden of inflation. We find that price changes from discounts mitigated the inflation burden while cheapflation exacerbated it. Content Type(s): Staff research, Staff working papers JEL Code(s): E, E2, E21, E3, E30, E31, L, L8, L81 Research Theme(s): Financial markets and funds management, Market functioning, Monetary policy, Inflation dynamics and pressures
On the Evolution of the United Kingdom Price Distributions Staff working paper 2018-25 Ba M. Chu, Kim Huynh, David T. Jacho-Chávez, Oleksiy Kryvtsov We propose a functional principal components method that accounts for stratified random sample weighting and time dependence in the observations to understand the evolution of distributions of monthly micro-level consumer prices for the United Kingdom (UK). Content Type(s): Staff research, Staff working papers JEL Code(s): C, C1, C14, C8, C83, E, E3, E31, E37 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures
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
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
Monetary Policy Transmission with Endogenous Central Bank Responses in TANK Staff working paper 2025-21 Lilia Maliar, Chris Naubert We study how the transmission of monetary policy innovations is affected by the endogenous response of the central bank to macroeconomic aggregates in a two-agent New Keynesian model. We focus on how the stance of monetary policy and the fraction of savers in the economy affect transmission. Content Type(s): Staff research, Staff working papers JEL Code(s): C, C6, C61, C62, C63, E, E3, E31, E5, E52 Research Theme(s): Models and tools, Economic models, Monetary policy, Monetary policy framework and transmission
Did the Renewable Fuel Standard Shift Market Expectations of the Price of Ethanol? Staff working paper 2017-35 Christiane Baumeister, Reinhard Ellwanger, Lutz Kilian It is commonly believed that the response of the price of corn ethanol (and hence of the price of corn) to shifts in biofuel policies operates in part through market expectations and shifts in storage demand, yet to date it has proved difficult to measure these expectations and to empirically evaluate this view. Content Type(s): Staff research, Staff working papers JEL Code(s): Q, Q1, Q18, Q2, Q28, Q4, Q42, Q5, Q58 Research Theme(s): Financial markets and funds management, Market functioning, Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures
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