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517 Results

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.

A Blueprint for the Fourth Generation of Bank of Canada Projection and Policy Analysis Models

Staff Discussion Paper 2023-23 Donald Coletti
The fourth generation of Bank of Canada projection and policy analysis models seeks to improve our understanding of inflation dynamics, the supply side of the economy and the underlying risks faced by policy-makers coming from uncertainty about how the economy functions.

Three things we learned about the Lynx payment system

Staff Analytical Note 2023-14 Nikil Chande, Zhentong Lu, Hiru Rodrigo, Phoebe Tian
Canada transitioned to a new wholesale payment system, Lynx, in August 2021. Lynx is based on a real-time settlement model that eliminates credit risk in the system. This model can require more liquidity; however, Lynx’s design allows Canada’s wholesale payments to settle efficiently.

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

Digitalization: Implications for Monetary Policy

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.

Unmet Payment Needs and a Central Bank Digital Currency

We discuss the payment habits of Canadians both in the current payment environment and in a hypothetical cashless environment.

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.
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