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 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
Redefining Financial Inclusion for a Digital Age: Implications for a Central Bank Digital Currency Staff Discussion Paper 2023-22 Alexandra Sutton-Lalani, Sebastian Hernandez, John Miedema, Jiamin Dai, Badr Omrane We explore quantitative and qualitative information about Canadians who face barriers to making digital payments. We also consider the implications of ongoing digitalization for modern financial inclusion and a potential central bank digital currency. Content Type(s): Staff research, Staff discussion papers Topic(s): Accessibility, Bank notes, Central bank research, Digital currencies and fintech, Digitalization, Financial services JEL Code(s): A, A1, A14, E, E4, E42, E5, E50, I, I3, I31, O, O3, O33, O5, O51
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 Topic(s): Econometric and statistical methods, Financial markets, Housing JEL Code(s): A, C, C4, C45, C5, C53, D, D2, R, R2, R3
Estimating Policy Functions in Payments Systems Using Reinforcement Learning Staff Working Paper 2021-7 Pablo S. Castro, Ajit Desai, Han Du, Rodney J. Garratt, Francisco Rivadeneyra 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. Content Type(s): Staff research, Staff working papers Topic(s): Digital currencies and fintech, Financial institutions, Financial system regulation and policies, Payment clearing and settlement systems JEL Code(s): A, A1, A12, C, C7, D, D8, D83, E, E4, E42, E5, E58