Assessing the Business Outlook Survey Indicator Using Real-Time Data
Bibliographic record
Abstract
Every quarter, the Bank of Canada conducts quarterly consultations with businesses across Canada, referred to as the Business Outlook Survey (BOS). A principal-component analysis conducted by Pichette and Rennison (2011) led to the development of the BOS indicator, which summarizes survey results and is used by the Bank as a gauge of overall business sentiment. In this paper, we examine whether data vintages matter when assessing the predictive content of the BOS indicator and individual BOS questions and whether the BOS is a better indicator of revised or unrevised macroeconomic data. As an indicator of business sentiment in the context of monetary policy, the reliability of the BOS is essential, and it is crucial to understand whether the signals it sends are best interpreted for early-released or revised data. For this purpose, we use different methods of forecasting that take into account the real-time perspective of the data. Results from the different methods show that the BOS content is informative regardless of data revisions. However, in real time, the BOS indicator and individual BOS questions are found to produce better nowcasts of first-released data or partially revised data than of latest-available data. This is particularly important in the case of growth in real business investment. In fact, because revisions to real business investment are more volatile than revisions to real gross domestic product (GDP), the choice of data vintages when assessing the ability of the BOS to forecast growth appears to be more important for real business investment than for real GDP.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".