Nowcasting Canadian GDP with Density Combinations
Bibliographic record
Abstract
Assessing the state of the economy in real time is critical for policy-making, and understanding the risks to those assessments is equally important. Policy-makers are typically provided with point forecasts that contain insufficient information about risks. In contrast, predictive densities estimate the entire range of possible outcomes. This provides a method for quantifying not only the current state of the economy but also the degree of uncertainty, the tail risks and the overall balance of risks around that state. Accordingly, this paper extends the framework of Chernis and Sekkel (2018) to produce density nowcasts for Canadian real GDP growth. We compare several methods of combining predictive densities from 98 models representing four popular classes of nowcasting models. The performance of these combinations is then assessed in both real-time and pseudo-real-time out-of-sample exercises, with the limited sample real-time simulations reinforcing the importance of data revisions for nowcasting. We demonstrate that the combined densities are reliable and accurate tools for assessing the state of the economy and risks to the outlook. We highlight in particular risks at the start of the COVID-19 pandemic.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".