Econometric and statistical methods

• Finding the balance—measuring risks to inflation and to GDP growth

  Staff Analytical Note 2023-18 Bruno Feunou, James Kyeong

  Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023.

  Content Type(s): Staff research, Staff analytical notes 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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.

  Content Type(s): Staff research, Staff discussion papers Research Topic(s): Bank notes, Digital currencies and fintech, Econometric and statistical methods JEL Code(s): C, C8, C81, E, E4, O, O5, O51