Fundamentals versus the leading index–the forecasting of Canada's output growth since 1991: an encompassing approach
Bibliographic record
Abstract
We evaluate the ability of Statistics Canada's Composite Leading Index to forecast Canada's real Gross Domestic Product (GDP) growth rate in the backdrop of the Duguay (1994 Duguay, D. 1994. Empirical evidence on the strength of the monetary transmission mechanism in Canada: an aggregative approach. Journal of Monetary Economics, 33: 39–61. [Crossref] , [Google Scholar], JME) model and also the dynamic and the error-correction variants of the Duguay model, that already include the ‘fundamentals’ of the Canadian business cycle. The results show that integrating the index in these models substantially improve the in-sample fit of the models and also provide an explanation for the ‘perplexingly’ large influence of the US real GDP on aggregate spending in the Canadian economy. Out-of-sample forecasts over the inflation-targeting regime (January 1991–April 2004), evaluated using the forecast encompassing tests, confirm that the index contains an important amount of new information about the future growth rate, quite apart from the information contained in the fundamentals.
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 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".