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
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
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
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
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
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
August 15, 2013 Big Data Analysis: The Next Frontier Bank of Canada Review - Summer 2013 Nii Ayi Armah The formulation of monetary policy at the Bank of Canada relies on the analysis of a broad set of economic information. Greater availability of immediate and detailed information would improve real-time economic decision making. Technological advances have provided an opportunity to exploit “big data” - the vast amount of digital data from business transactions, social media and networked computers. Big data can be a complement to traditional information sources, offering fresh insight for the monitoring of economic activity and inflation. Content Type(s): Publications, Bank of Canada Review articles JEL Code(s): C, C5, C53, C6, C63, C8, C80
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
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
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