The Comparative Ex Post Forecasting Properties of Several Canadian Quarterly Econometric Models
Bibliographic record
Abstract
In this study we compare the forecasting ability of the three publicly available Canadian quarterly econometric models: The AERIC Short-Term Quarterly Forecasting Model of the Canadian Economy (AERIC) developed in The Conference Board in Canada, the Quarterly Econometric Model of the Canadian Economy (QFM) developed at the University of Toronto, and the Research Department quarterly experimental econometric model of the Canadian economy (RDX2) developed in the Bank of Canada. The standards against which these econometric models are measured are univariate Box-Jenkins models and a monetarist reduced form model. Sixteen variables of general interest to forecasters are examined over various prediction intervals so as to ascertain the forecast errors in the levels of the variables and their percentage changes. We find that no one model predominates. Although the three econometric models generally perform well in comparison with the Box-Jenkins models, the monetarist model consistently predicts nominal gross national expenditure best. Among the three econometric models there is considerable variation in the ability to predict the variables over different time horizons.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".