Monetary Policy and Inflation Dynamics: A Comparative Analysis of Pre- and Post-Pandemic Periods in Poland and in Selected Economies
Bibliographic record
Abstract
Goal – The study assesses the effectiveness of Poland’s monetary policy in curbing inflation between 2015–2023, focusing on post-pandemic structural shifts. It verifies the effectiveness of the National Bank of Poland’s (NBP) conventional (interest rates) and unconventional (QE) tools, considering the international context and the structural nature of inflation. Research methodology – The methodology relies on a Vector Autoregression (VAR) model and Granger causality tests on quarterly data (2015–2023). This quantitative analysis is deepened by insights from structural (DSGE) and agent-based (ABM) models, comparative analysis, and qualitative analysis of central bank communication. Score/results – Result Interest rate hikes effectively, though with a 4–6 quarter lag, limited inflation; a temporary ‘price puzzle’ was noted. QE’s impact on inflation was short-lived and marginal. External cost shocks were confirmed as the dominant driver of Poland’s inflation (structural models attribute 8–10 p.p. of the 16.1% peak to them). NBP’s policy was necessary to anchor expectations, but its effectiveness was limited by global factors. The analysis also revealed a “distributional trilemma”. Originality/value – The article combines detailed econometric analysis for Poland (VAR, Granger) with a broad international context and an in-depth analysis of transmission mechanisms under dominant external supply shocks. It integrates findings from VAR, DSGE, and ABM models to explain post-pandemic inflation dynamics.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".