Fiscal deficits and the socioeconomic consequences of rebalancing: Insights from a <scp>TVP‐VAR</scp> with stochastic volatility
Bibliographic record
Abstract
Abstract This article connects two salient economic features: (i) Fiscal shocks have asymmetric effects across business cycle phases (Gechert, Horn, & Paetz, 2019); (ii) the unemployment‐output trade‐off is time varying and may be unstable. The intertwined dynamic behaviour of fiscal deficit shocks and the unemployment‐output trade‐off is studied in this article using a time‐varying parameter (TVP) vector autoregression (VAR) with stochastic volatility techniques applied to the analysis of data from Canada, France, Germany, Japan, Spain, Sweden, United Kingdom and the United States of America. We confirm the trade‐off heterogeneity across country, and its time‐varying nature across time, showing in addition its fluctuation around a long‐run reference value. We document significant short‐run impacts of fiscal shocks on the unemployment‐output trade‐off which, based on the experience of the Global Financial Crisis, becomes larger in periods of economic turmoil. Policy‐wise, the rebalancing of public finances may have unexpected adverse effects on job creation if implemented during slumps, precisely when the labour market sensitivity with respect to the performance of the product market is likely to be more acute. This message is particularly relevant in the aftermath of the Covid‐19 pandemic.
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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 | 0.006 |
| 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 | 0.001 |
| 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".