Convergence Within the EU: Evidence from Interest Rates
Bibliographic record
Abstract
The economic and political changes which are taking place in Europe affect interest rates. This paper develops a two-factor model for the term structure of interest rates specially designed to apply to EMU countries. In addition to the participant countries' short-term interest rate, we include as a second factor a European short-term interest rate. We assume that the European rate follows a mean reverting process. The domestic interest rate also follows a mean reverting process, but its convergence is to a stochastic mean which is identified with the European rate. Closed-form solutions for prices of zero coupon discount bonds and options on these bonds are provided. A special feature of the model is that both the domestic and the European interest rate risks are priced. We also discuss an empirical estimation focusing on the Spanish bond market. The European rate is proxied by the ecu's interest rate. Through a comparison of the performance of our convergence model with a Vasicek model for the Spanish bond market, we show that our model provides a better fit both in-sample and out-of sample and that the difference in performance between the models is greater the longer the maturity of the bonds.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".