The Term Spread International Evidence of Non-Linear Adjustment
Bibliographic record
Abstract
This study tests whether changes in the short-term interest rate can best be modelled in a nonlinear fashion. We argue that there are good theoretical and empirical reasons for adopting this strategy. Using monthly data from several industrialized countries, namely Canada, Germany, Sweden, Switzerland, UK, and US, we show that the short-term interest rate movements are better explained, usually via the exponential smooth transition autoregression (ESTR). Unlike the existing literature on non-linear estimation, we consider a number of candidates for the transition variable. These include: an error correction term, estimated from an underlying cointegrating relationship predicted by the expectations hypothesis, the US spread, the domestic spread, inflation and output growth forecasts, and deviations from an inflation target in the case of Canada, the UK and Sweden. The sample spans the period from 1960-1998. We cannot reject non-linearity in the behavior of interest rate changes most often when the (lagged) domestic spread serves as the transition variable. In the case of the inflation targeting countries in our sample, the most appropriate transition variable can be the deviation from the publicly announced inflation target. We supplement estimates with extensive diagnostic testing to ensure that we can reject the linear alternative with reasonable confidence. We believe that changes in central bank policies and in the reaction of market participants over time to such changes argue in favor of the non-linear estimation approach. We would also argue that any model of the term spread over a fairly long span of time necessitates resort to non-linear estimation methods.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".