Forecasting Canadian GDP growth using XGBoost
Bibliographic record
Abstract
The objective of this paper is to apply state-of-the-art machine-learning (ML) algorithms to predict the monthly and quarterly real GDP growth of Canada using both Google Trends (GT) and Official data that are available ahead of the release of GDP data by Statistics Canada. This paper applies a novel approach for selecting features with Extreme Gradient Boosting (XGBoost) using the AutoML function of H2O. For this purpose, 5000 to 15000 XGBoost models are trained using this function. We use a very rigorous variable selection procedure, where only the best features are selected into the next stage to build a final learning model. Then pertinent features are introduced into XGBoost for forecasting real GDP growth rate. The forecasts are further improved by using Principal Component Analysis (PCA) to choose the best factors out of the predictors selected by XGBoost. The results indicate that there are gains in nowcasting accuracy from using XGBoost with this two-step strategy. We first find that XGBoost is a superior algorithm for forecasting relative to our baseline methods, such as autoregression and other standard boosting algorithms. We also find that Google Trends data provides a very viable source of information for predicting Canadian real GDP growth with XGBoost when Official data are not yet available due to publication lags. Therefore, we can forecast real GDP growth rate accurately ahead of the release of Official data. Moreover, we apply various techniques to make the machine learning model more interpretable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".