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 Research Topic(s): Econometric and statistical methods, Market structure and pricing 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
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. Content Type(s): Staff research, Staff analytical notes Research Topic(s): Central bank research, Econometric and statistical methods, Economic models JEL Code(s): A, A1, A10, B, B2, B23, C, C4, C45, C5, C55 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity
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 Research Topic(s): Econometric and statistical methods, Firm dynamics 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
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. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Econometric and statistical methods, Financial markets, Housing JEL Code(s): A, C, C4, C45, C5, C53, D, D2, R, R2, R3 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
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. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Business fluctuations and cycles, Econometric and statistical methods JEL Code(s): C, C3, C32, C5, C58, E, E4, E44, G, G1, G17 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting
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 Research Topic(s): Econometric and statistical methods JEL Code(s): C, C1, C11, C5, C52, C53, E, E3, E37 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Real economy and forecasting
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. Content Type(s): Staff research, Staff working papers Research Topic(s): Econometric and statistical methods, Firm dynamics, Market structure and pricing JEL Code(s): L, L1, L13, L4, L42, L5, L51 Research Theme(s): Financial markets and funds management, Market structure, Models and tools, Econometric, statistical and computational methods, Structural challenges, Digitalization and productivity
Digitalization: Implications for Monetary Policy Staff discussion paper 2023-18 Vivian Chu, Tatjana Dahlhaus, Christopher Hajzler, Pierre-Yves Yanni We explore the implications of digitalization for monetary policy, both in terms of how monetary policy affects the economy and in terms of data analysis and communication with the public. Content Type(s): Staff research, Staff discussion papers Research Topic(s): Digitalization, Inflation and prices, Market structure and pricing, Monetary policy, Monetary policy communications, Monetary policy transmission JEL Code(s): C, C4, C8, E, E3, E31, E32, E5, E52 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Economic models, Monetary policy, Monetary policy framework and transmission, Structural challenges, Digitalization and productivity
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. Content Type(s): Staff research, Staff working papers Research Topic(s): Asset pricing, Econometric and statistical methods JEL Code(s): C, C5, C58, G, G1, G12 Research Theme(s): Financial markets and funds management, Market functioning, Models and tools, Econometric, statistical and computational methods
Global Demand and Supply Sentiment: Evidence from Earnings Calls Staff working paper 2023-37 Temel Taskin, Franz Ulrich Ruch This paper quantifies global demand, supply and uncertainty shocks and compares two major global recessions: the 2008–09 Great Recession and the COVID-19 pandemic. We use two alternate approaches to decompose economic shocks: text mining techniques on earnings calls transcripts and a structural Bayesian vector autoregression model. Content Type(s): Staff research, Staff working papers Research Topic(s): Business fluctuations and cycles, Coronavirus disease (COVID-19), Econometric and statistical methods, Inflation and prices, International topics JEL Code(s): C, C1, C11, C3, C32, E, E3, E32, G, G1, G10 Research Theme(s): Models and tools, Econometric, statistical and computational methods, Monetary policy, Inflation dynamics and pressures, Real economy and forecasting