Fiscal Adjustment Heterogeneity in Inflationary Conditions in the Eurozone: A Non-Stationary Heterogeneous Panel Approach
Bibliographic record
Abstract
In recent years, fiscal policy in the Eurozone (EZ) has faced challenges posed by the strong and rapid increase in inflation as a consequence of the COVID-19 pandemic and other geo-political crises. Due to the fear of “fiscal inflation” present during episodes of fiscal stimulus during the pandemic crisis, this paper assesses the relationship between discretionary fiscal policy and inflation in developed EZ economies, taking into consideration the rise in energy prices as a control variable. This study considers the econometric framework of heterogeneous, non-stationary panels (Pooled Mean Group (PMG) and Common Correlated Effects Mean Group (CCEMG) estimators). Using quarterly panel data for the period 2015q1–2024q1, the results show that, in the long run, the effects of fiscal policy on inflation are insignificant. However, covering only the pandemic and other geo-political crises (2020q1–2024q1), research shows a significant negative long-run relationship between fiscal expenditure and inflation and heterogeneous short-run fiscal adjustments due to the lack of a fiscal union in the EU economies. Hence, accompanied by monetary policy, the discretionary response of fiscal policy to inflationary shock was oriented in the same direction—the reduction in inflationary pressures during a geo-political crisis. Fiscal policy mitigated inflationary pressures in these recent crises, while in the long run, it did not affect nominal variables, indicating that there is no evidence of fiscal inflation in the sample of EZ economies during a stabilization period or under crisis conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".