Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/297090 
Year of Publication: 
2023
Series/Report no.: 
Bank of Canada Staff Discussion Paper No. 2023-21
Publisher: 
Bank of Canada, Ottawa
Abstract: 
This paper examines whether machine learning (ML) algorithms can outperform a linear model in predicting monthly growth in Canada of both house prices and existing home sales. The aim is to apply two widely used ML techniques (support vector regression and multilayer perceptron) in economic forecasting to understand their scopes and limitations. We find that the two ML algorithms can perform better than a linear model in forecasting house prices and resales. However, the improvement in forecast accuracy is not always statistically significant. Therefore, we cannot systematically conclude using traditional time-series data that the ML models outperform the linear model in a significant way. Future research should explore non-traditional data sets to fully take advantage of ML methods.
Subjects: 
Econometric and statistical methods
Financial markets
Housing
JEL: 
A
C45
C53
R2
R3
D2
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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