What caused the euro area post-pandemic inflation? An application of Bernanke and Blanchard (2023)
Bibliographic record
Abstract
This paper applies the semi-structural model proposed by Bernanke and Blanchard (2023) to analyse wage growth, price inflation and inflation expectations in the euro area. It is part of a broader project coordinated by Bernanke and Blanchard to provide a unified framework for analysing and comparing global inflation dynamics across the major world economic areas, including US, euro area, Canada, UK, and Japan. The paper makes four main contributions. First, it estimates the model using quarterly data from the euro area covering the period from the first quarter of 1999 to the second quarter of 2023. Second, it conducts an empirical assessment of how euro area price inflation responds to various exogenous shocks. This includes evaluating how shock transmission evolved during the pandemic and comparing it with experience in the United States. Third, the model decomposes the drivers of wage growth and price inflation in the post-pandemic period. It emphasises the transmission channels and the respective roles of supply and demand forces that have contributed to the recent inflationary surge. Notably, it identifies the impact of labour market tightness, productivity, global supply chain disruptions and energy and food price shocks. Finally, the model generates conditional projections based on these exogenous shocks, enabling a more robust cross-check of inflation forecasts during times of significant global economic disturbances.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".