Testing for causality in the transmission of Eurodollar and US interest rates
Bibliographic record
Abstract
This article employs linear Granger causality tests and the nonlinear causality test of Baek and Brock (1992 Baek, E and Brock, W. 1992. A general test for Granger causality: bivariate model. Technical Report. Iowa State University and University of Wisconsin Madison [Google Scholar]) and Hiemstra and Jones (1994 Hiemstra, C and Jones, JD. 1994. Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance, 49: 1639–64. [Crossref], [Web of Science ®] , [Google Scholar]), as recently modified by Diks and Panchenko (2005b Diks, C and Panchenko, V. 2005b. A new statistic and practical guidelines for nonparametric Granger causality testing, mimeo, Department of Economics, University of Amsterdam. [Google Scholar]), to re-examine the dynamic relation between daily Eurodollar and US certificate of deposit interest rates during the period 4 January 1971 to 15 July 2005. Although we find significant linear causality only from the US certificate of deposit interest rates to the Eurodollar interest rates, we find significant bidirectional nonlinear causality between Eurodollar and US certificate of deposit interest rates.
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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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.005 | 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".