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 state of the average outlook for 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 an assessment of the role of uncertainty for a path of innovations consistent with the increase in the observed VSTOXX measure of uncertainty since 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 around 9.2% in the fourth quarter of 2020, and subsequently to recover with a rebound to pre-crisis levels roughly in June 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. We conduct simulations that quantify the potential benefit of recovered confidence in reducing the uncertainty-induced losses associated with a possible third wave of the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".