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Forecasting Canadian GDP Growth with Machine Learning

Shafiullah Qureshi (), Ba Chu () and Fanny S. Demers ()
Additional contact information
Shafiullah Qureshi: Department of Economics, Carleton University, https://carleton.ca/economics/people/qureshi-shafiullah/
Ba Chu: Department of Economics, Carleton University, https://carleton.ca/economics/people/chu-ba-m/
Fanny S. Demers: Department of Economics, Carleton University, https://carleton.ca/economics/people/demers-fanny-s/

No 21-05, Carleton Economic Papers from Carleton University, Department of Economics

Abstract: This paper applies state-of-the-art machine learning (ML) algorithms to forecast monthly real GDP growth in Canada by using both Google Trends (GT) data and official macroeconomic data (which are available ahead of the release of GDP data by Statistics Canada). We show that we can forecast real GDP growth accurately ahead of the release of GDP figures by using GT and official data (such as employment) as predictors. We first pre-select features by applying up-to-date techniques, namely, XGBoost’s variable importance score, and a recent variable-screening procedure for time series data, namely, PDC-SIS+. These pre-selected features are then used to build advanced ML models for forecasting real GDP growth, by employing tree-based ensemble algorithms, such as XGBoost, LightGBM, Random Forest, and GBM. We provide empirical evidence that the variables pre-selected by either PDC-SIS+ or the XGBoost’s variable importance score can have a superior forecasting ability. We find that the pre-selected GT data features perform as well as the pre-selected official data features with respect to short-term forecasting ability, while the pre-selected official data features are superior with respect to long-term forecasting ability. We also find that (1) the ML algorithms we employ often perform better with a large sample than with a small sample, even when the small sample has a larger set of predictors; and (2) the Random Forest (that often produces nonlinear models to capture nonlinear patterns in the data) tends to under-perform a standard autoregressive model in several cases while there is no clear evidence that the XGBoost and the LightGBM can always outperform each other.

Pages: 22 pages
Date: 2021-05-17
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-for
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Published: Carleton Economics Papers

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