Prévision de l’activité économique au Québec
Bibliographic record
Abstract
We evaluate the predictability of the economic activity of Quebec in a data-rich environment. In our framework, the province of Quebec is treated as a regional economy that is exposed to the influence of the Canadian and American economies. Three large information sets are used: data from Quebec, Canada and the US, for a total of 453 macroeconomic variables. We compare a large number of models for the purpose of identifying those that are most efficient at forecasting macroeconomic aggregates of Quebec such as the GDP, employment, inflation, investment, etc. Our results suggest that the best model in terms of mean squared error depends on the variable of interest and on the forecasting horizon. A model that performs well at short horizons does not necessarily perform well at long horizons. Likewise, the model that best predicts the nominal GDP does not necessarily win the race when it comes to predict the real GDP. The ARMA(1,1) is found to be one of the best standard models to predict the nominal GPD and inflation. Models exploiting rich data sets often rank best individually. The most robust performances are obtained by averaging the forecasts delivered by the 5, 10 or 20 best individual models. Nous evaluons la previsibilite de l’activite economique du Quebec dans un environnement riche en donnees. Notre approche consiste a voir la province du Quebec comme une economie regionale soumise aux influences des economies canadienne et americaine. Trois grands ensembles d’information sont utilises : les donnees quebecoises, canadiennes et americaines, soit un total de 453 variables macroeconomiques. Nous comparons un grand ensemble de modeles dans le but d’identifier ceux qui sont les plus efficaces pour predire les principaux agregats de l’economie quebecoise tels que le PIB, l’emploi, l’inflation, l’investissement, etc. Nos resultats suggerent que le meilleur modele en termes d’erreur quadratique moyenne depend de la serie a predire et de l’horizon de prevision vise. Un modele ayant de bonnes performances a court horizon peut devenir moins bon a long horizon. Un modele bon pour predire le PIB nominal ne l’est pas forcement pour predire le PIB reel. Dans la categorie des modeles standards, le modele ARMA(1,1) s’est revele un bon benchmark pour predire le PIB nominal ou l’inflation. Les modeles riches en donnees se classent souvent comme les meilleurs individuellement. La moyenne des previsions fournies par une selection des 5, 10 ou 20 meilleurs modeles individuels delivre des performances encore plus robustes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".