ULOGA RASTA PREMIJA ŽIVOTNOG I NEŽIVOTNOG OSIGURANJA U EKONOMSKOM RASTU ZEMALJA EVROSPKE UNIJE: PANEL ARDL ANALIZA
Bibliographic record
Abstract
Insurance plays a crucial role in economic development by providing financial security, encouraging investment, and reducing risks in the economy. In particular, the growth of life and non-life insurance premiums can contribute to economic stability and GDP growth by mobilizing capital and improving market efficiency. The aim of this analysis is to examine whether and to what extent the growth rates of gross life and non-life insurance premiums contribute to GDP growth in the European Union countries, both in the long and short run. The research is based on quarterly panel data for European Union countries, covering the period from the fourth quarter of 2017 to the fourth quarter of 2024. To analyze these interdependencies, the panel ARDL (Autoregressive Distributed Lag) model is employed, allowing for a consistent and efficient estimation of both long- and short-run effects of insurance on economic growth. This model accommodates variables of the same or different levels of integration, provided they are below I(2), ensuring more precise results. Given the limitations of the software environment, parameter estimation is conducted using the Pooled Mean Group (PMG) method, which allows for a reliable interpretation of the long-term relationship between insurance and economic growth while permitting heterogeneity in short-term effects across observational units.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".