Composite Likelihood for Stochastic Migration Model with Unobserved\n Factor
Bibliographic record
Abstract
We introduce the conditional Maximum Composite Likelihood (MCL) estimation\nmethod for the stochastic factor ordered Probit model of credit rating\ntransitions of firms. This model is recommended for internal credit risk\nassessment procedures in banks and financial institutions under the Basel III\nregulations. Its exact likelihood function involves a high-dimensional\nintegral, which can be approximated numerically before maximization. However,\nthe estimated migration risk and required capital tend to be sensitive to the\nquality of this approximation, potentially leading to statistical regulatory\narbitrage. The proposed conditional MCL estimator circumvents this problem and\nmaximizes the composite log-likelihood of the factor ordered Probit model. We\npresent three conditional MCL estimators of different complexity and examine\ntheir consistency and asymptotic normality when n and T tend to infinity. The\nperformance of these estimators at finite T is examined and compared with a\ngranularity-based approach in a simulation study. The use of the MCL estimator\nis also illustrated in an empirical application.\n
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".