Credit-to-GDP ratios – non-linear trends and persistence: evidence from 44 OECD economies
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| 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".