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383 Results

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.

Machine learning for economics research: when, what and how

Staff Analytical Note 2023-16 Ajit Desai
This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used.

Identifying Nascent High-Growth Firms Using Machine Learning

Staff Working Paper 2023-53 Stephanie 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.

Predicting Changes in Canadian Housing Markets with Machine Learning

Staff Discussion Paper 2023-21 Johan Brannlund, Helen Lao, Maureen MacIsaac, Jing Yang
We apply two machine learning algorithms to forecast monthly growth of house prices and existing homes sales in Canada. Although the algorithms can sometimes outperform a linear model, the improvement in forecast accuracy is not always statistically significant.

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.

Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis

Staff Working Paper 2023-45 Tony Chernis
I show how to combine large numbers of forecasts using several approaches within the framework of a Bayesian predictive synthesis. I find techniques that choose and combine a handful of forecasts, known as global-local shrinkage priors, perform best.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C5, C52, C53, E, E3, E37

Competition for Exclusivity and Customer Lock-in: Evidence from Copyright Enforcement in China

Staff Working Paper 2023-43 Youming Liu
This paper studies the music streaming industry and argues that having exclusive rights granted by copyright law drives firms to offer exclusive content to lock in customers. I employ theoretical and descriptive empirical analysis, along with a dynamic structural model, to support the argument and explore policies for improving competition.

Generalized Autoregressive Gamma Processes

Staff Working Paper 2023-40 Bruno Feunou
We introduce generalized autoregressive gamma (GARG) processes, a class of autoregressive and moving-average processes in which each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. We show that using GARG processes reduces pricing errors by substantially more than using existing autoregressive gamma processes does.

Cryptoasset Ownership and Use in Canada: An Update for 2022

Staff Discussion Paper 2023-14 Daniela Balutel, Christopher Henry, Doina Rusu
We find that Bitcoin ownership declined from 13% in 2021 to 10% in 2022. This drop occurred against a background of steep price declines and an increasingly tight regulatory atmosphere.

Is Climate Transition Risk Priced into Corporate Credit Risk? Evidence from Credit Default Swaps

Staff Working Paper 2023-38 Andrea Ugolini, Juan C. Reboredo, Javier Ojea Ferreiro
We study whether the credit derivatives of firms reflect the risk from climate transition. We find that climate transition risk has asymmetric and significant economic impacts on the credit risk of more vulnerable firms, and negligible effects on other firms.
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