Do Firms’ Sales Expectations Hit the Mark? Evidence from the Business Leaders’ Pulse
Bibliographic record
Abstract
This paper replicates and extends the work of Altig et al. (2022) on firms’ subjective sales growth expectations using Canadian survey data from the Bank of Canada’s Business Leaders’ Pulse. We examine the formation, uncertainty and predictive validity of firm-level sales growth forecasts using subjective probability distributions from business leaders at a one-year-ahead horizon. The replication work performed here confirms several findings from Altig et al. (2022), including that expected sales growth predicts realized sales growth, subjective uncertainty predicts forecast errors and firms frequently revise their expectations, usually by small amounts. We also find that subjective uncertainty predicts the magnitude of forecast revisions and follows a V-shaped relationship with past sales growth. We extend the original analysis by further demonstrating that firms with weaker recent performance assign greater weight to future weak growth scenarios, and subsequently that these firms are more likely to underperform, suggesting expectations are grounded in real conditions. The results presented in this paper reinforce the value of firm-level survey data for macroeconomic forecasting and policy analysis and help validate the Business Leaders’ Pulse as a reliable source of firm-level expectations data.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".