Firm‐Level Expectations and Macroeconomic Conditions: Underpinnings and Disagreement
Bibliographic record
Abstract
ABSTRACT There is abundant evidence that financial analysts' inflation expectations differ in economically important ways from those of nonfinancial specialists. As a result, there is an increasing demand for firm‐level data to more accurately capture the views of price setters. The unusually rich firm‐level survey data from South Africa allow us to explore some of the ways in which the expectations of firms differ from those of other groups surveyed. We focus specifically on forecast disagreement, which can offer insights into the level of uncertainty reflected in the data and the degree to which expectations are influenced by the policy regime in place. We find that the divergence in inflation forecasts among respondents is partly explained by differences in how respondents believe the broader macroeconomy is evolving. The effect of aggregating the data in different ways is also considered. When we construct a new measure of macroeconomic disagreement that combines all the variables being forecast, we are able to see that forecasters responded sharply in early 2020 as the COVID‐19 pandemic emerged.
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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 | 0.011 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".