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
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
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
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
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
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
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
Modelling Canadian mortgage debt and payments in a semi-structural model Staff analytical note 2024-1 Fares Bounajm, Austin McWhirter We show how Canadian mortgage debt dynamics can be modelled in a semi-structural macroeconomic model, such as the Bank of Canada’s LENS. The model we propose accounts for Canada’s unique mortgage debt structure. Content Type(s): Staff research, Staff analytical notes JEL Code(s): E, E2, E27, E4, E43, E47, G, G5, G51 Research Theme(s): Financial system, Household and business credit, Models and tools, Economic models, Monetary policy, Monetary policy framework and transmission
What COVID-19 revealed about the resilience of bond funds Staff analytical note 2020-18 Guillaume Ouellet Leblanc, Ryan Shotlander The liquidity management strategies of fund managers, supported by policy measures, have helped bond funds limit the increase in redemptions caused by COVID 19. This avoided further deterioration in liquidity in bond markets. Nevertheless, these funds were left with lower cash buffers, which could make them more vulnerable to additional large redemptions. Content Type(s): Staff research, Staff analytical notes JEL Code(s): G, G1, G2, G20, G23 Research Theme(s): Financial markets and funds management, Market functioning, Financial system, Financial institutions and intermediation, Financial stability and systemic risk
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