STATIONARITY OF LONG-TERM REAL INTEREST RATES: FINDINGS FROM NONLINEAR FOURIER UNIT ROOT TEST
Bibliographic record
Abstract
This study aims to investigate the stationarity of long-term real interest rates for the top 10 countries with the highest long-term real interest rates among OECD countries in order to determine the effectiveness of monetary policies to be implemented. The study is one of the first to consider both structural breaks and nonlinearity to determine the effectiveness of policies to be implemented regarding interest rates. As a result of the Ranjbar et al. (2018) unit root test allowing for both structural changes and nonlinearity, the long-term real interest rates are stationary at the level for Turkey and Colombia; whereas not stationary at the level for the USA, Chile, Hungary, Iceland, Korea, Norway, Poland, and Canada. According to the results, it was determined that the policies to be implemented regarding interest rates in Türkiye and Colombia would be ineffective because interest rates tend to be mean-reverting.
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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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".