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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".