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2166 Results

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.

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.
June 2, 2022

Economic progress report: Navigating a high inflation environment

Remarks (delivered virtually) Paul Beaudry Gatineau Chamber of Commerce Gatineau, Quebec
Bank of Canada Deputy Governor Paul Beaudry talks about the Bank’s latest interest rate announcement and the importance of keeping inflation expectations well anchored to prevent high inflation from becoming entrenched.

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.

Inference in Games Without Nash Equilibrium: An Application to Restaurants’ Competition in Opening Hours

Staff working paper 2018-60 Erhao Xie
This paper relaxes the Bayesian Nash equilibrium (BNE) assumption commonly imposed in empirical discrete choice games with incomplete information. Instead of assuming that players have unbiased/correct expectations, my model treats a player’s belief about the behavior of other players as an unrestricted unknown function. I study the joint identification of belief and payoff functions.

Estimating Policy Functions in Payments Systems Using Reinforcement Learning

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.

Forecasting Risks to the Canadian Economic Outlook at a Daily Frequency

Staff discussion paper 2023-19 Chinara Azizova, Bruno Feunou, James Kyeong
This paper quantifies tail risks in the outlooks for Canadian inflation and real GDP growth by estimating their conditional distributions at a daily frequency. We show that the tail risk probabilities derived from the conditional distributions accurately reflect realized outcomes during the sample period from 2002 to 2022.

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.
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