Real Exchange Rate Channel of QE Monetary Transmission Mechanism in Selected EU Members: The Pooled Mean Group Panel Approach
Bibliographic record
Abstract
Since the Great 2008 Recession, central banks around the world have been coping with monetary consequences that highlight structural costs of the economic system and the rise of unconventional monetary measures. This research aims to capture the heterogeneous effects of expansionary balance sheet (Quantitative easing) policy on the real effective exchange rate and current account balance under the different exchange rate regimes in crisis circumstances. The sample is structured of two groups of EU countries differentiated by level of monetary autonomy: EZ members (Austria, Belgium, France, Germany, Netherlands, Italy, and Spain) are represented by countries with the highest level of asset purchases by ECB and emerging monetary autonomous EU economies (Czech, Hungary, Poland, and Romania). Empirical findings are based on the framework of cross-sectional dependent, non-stationary, heterogeneous, dynamic panels using the (Pooled) Mean Group estimator during the 2014Q1–2023Q1 time horizon. Results indicate a positive long-run relationship between the central bank balance sheet assets, the real interest rate, and the real effective exchange rate. A negative long-term relationship with the current account balance is confirmed, suggesting a diminishing external position. While error-correction parameters are significant and heterogeneous, research confirms higher real effective exchange rate reaction for the EZ members with higher adjustment toward worsening competitiveness along with external balance.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".