The Relevance of Financial Integration Across Europe: A Dynamic Panel Data Approach
Bibliographic record
Abstract
This paper aims to examine the degree of financial integration for selected European countries from 1980 to 2019 using dynamic panel data approaches, covering the panel unit-root tests, cointegration analysis, panel Granger causality tests, and the DOLS and FMOLS methods.Saving and investment rates are found to be stationary at order of integration one, i.e., I (1).It is also found to be that the series are cointegrated along with different methods.Similar to that of cointegration analysis, it is observed that the causality between the variables is statistically significant and bidirectional.Finally, the current study proceeds to test the direction of coefficients for the estimation of the long-run relationship among the series by way of using panel DOLS and FMOLS methods.The values of coefficients show that the effects of each variable on another are both positive and statistically significant at the 1% level.Therefore, the empirical findings point out that strong financial integration is relevant to those European countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".