Interest rate pass-through: a nonlinear vector error-correction approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper analyzes pass-through from money market rates to consumer retail loan and deposit rates in Canada from 1983 to 2015 using a nonlinear vector error-correction model. This model permits estimation of long-run pass-through coefficients while simultaneously accounting for asymmetric adjustments and short-run dynamics. In contrast to empirical frameworks used in previous studies, it also allows testing of commonly made assumptions such as exogeneity of the market rate, making inference more robust. I find that pass-through was complete for all rates before the financial crisis although only after the mid 1990s for the 1 year mortgage rate. Since the end of the 2008–2009 recession, pass-through remains complete in the mortgage market but has significantly declined for deposit rates. Furthermore, many rates adjust asymmetrically but the direction of rigidity differs among rates and time periods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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