Credit-to-GDP ratios – non-linear trends and persistence: evidence from 44 OECD economies
Why this work is in the frame
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Bibliographic record
Abstract
Purpose In particular, in this article, the authors investigate the degree of persistence in the credit-to-gross domestic product (GDP) ratio in 44 Organisation for Economic Co-operation and Development (OECD) economies in the context of nonlinear deterministic trends. Design/methodology/approach The authors use Chebyshev's polynomials in time, which allow us to model changes in the data in a smoother way than by structural breaks. Findings This study’s results indicate that approximately one-quarter of the series display non-linear structures, and only Argentina displays a mean reverting pattern. Research limitations/implications Policy implications of the results obtained are discussed at the end of the manuscript. Originality/value The authors use an approach developed that allows for non-linear trends based on Chebyshev polynomials in time, with the residuals being fractionally integrated or integrated of order d , where d can be any real value.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it