The macroeconomic impact of asymmetric uncertainty shocks
Bibliographic record
Abstract
Political shocks impact the economy in different ways, depending of their nature. To capture these effects effectively, we present the Uncertainty Perception Indicator (UPI) based on German newspaper content. This approach combines the time-inherent stability of simple counts of articles with the thematic openness and flexibility of topic models. Using the dynamic RollingLDA technique facilitates the close-to-real-time identification of both the magnitude of an uncertainty shock and its specific characteristics. Hence, the UPI could prove highly useful for economic forecasters and policymakers, since it renders possible more timely and targeted policy reactions. Employing a Bayesian VAR approach, we analyze the effects of various UPI shocks on fixed investment and other macroeconomic variables. Our results document the asymmetric nature of uncertainty shocks, as their consequences are dependent on the respective sources of uncertainty. We find that international shocks only have weak effects on the German macroeconomy, while domestic policy shocks prove to be highly significant. These results markedly differ from earlier studies that, in the case of Germany, tend to maintain the opposite.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".