Asymmetric adjustments in the spread of lending and deposit rates: Evidence from extended threshold unit root tests
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Bibliographic record
Abstract
Abstract In this paper, we test for asymmetric adjustments in the spread of the U.S. prime lending rate and 3‐month certificate of deposit rate. In doing so, we extend the pioneering threshold unit root tests of Enders and Granger (1998) to more flexible models where the deterministic terms and short‐run dynamics, in addition to the persistent parameters, can differ in two regimes. While some previous works have tested for asymmetric adjustments in the spread of lending and deposit rates using threshold unit root tests, the deterministic terms and short‐run dynamics were assumed to be symmetric, which can lead to bias and less accurate conclusions if these conditions do not hold. Overall, we find that the spread in lending and deposit rates is stationary but adjustment to the equilibrium is asymmetric. In particular, we find more rapid adjustment when the spread is narrowing below a threshold level than when widening above this level. Several theoretical implications are suggested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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