An Improved Equation for Predicting Canadian Non-Commodity Exports
Bibliographic record
Abstract
We estimate two new equations for Canadian non-commodity exports (NCX) that incorporate three important changes relative to the current equation used at the Bank of Canada. First, we develop two new foreign activity measures (FAMs), which add new components to the FAM currently used at the Bank of Canada. The first measure adds US exports and US government expenditures, and the second adds US industrial production. These new FAMs calibrate the weights on the various components based on the 2014 World Input-Ouput Database to avoid the instability problem that arises when the equations are estimated. Second, we add a new variable to the equations, the trend of Canada’s manufacturing share of output, to control for structural or competitiveness factors that affect Canada’s global import market share. Third, the relative price of exports is determined by a new measure of the Canadian real effective exchange rate developed by Barnett, Charbonneau and Poulin-Bellisle (2016). We find that the new equations improve the in-sample fit and the out-of-sample forecast accuracy relative to the current equation specified in “LENS,” a forecasting model used at the Bank of Canada.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".