The impact of pessimistic expectations on the effects of COVID-19-Induced uncertainty in the euro area
Bibliographic record
Abstract
We estimate a monthly Interacted-VAR model for euro area macroeconomic aggregates allowing for the impact of uncertainty shocks to depend on the average outlook of the economy measured by survey data. We find that, in response to an uncertainty shock, the peak decrease in industrial production and inflation is around three and a half times larger during pessimistic times. We build scenarios for a path of innovations consistent with the increase in the observed VSTOXX measure of uncertainty at the outset of the COVID-19 epidemics in February and March 2020. Industrial production is predicted to experience a year-over-year peak loss of between 15.1% and 19% in the fourth quarter of 2020, and subsequently to recover with a rebound to pre-crisis levels between May and August 2021. The large impact is the result of an extreme shock to uncertainty occurring at a time of very negative expectations for the economic outlook.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".