Bayesian Analysis of Asymmetric Stochastic Conditional Duration Model
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Bibliographic record
Abstract
ABSTRACT This paper proposes Markov chain Monte Carlo methods to estimate the parameters and log durations of the correlated or asymmetric stochastic conditional duration models. Following the literature, instead of fitting the models directly, the observation equation of the models is first subjected to a logarithmic transformation. A correlation is then introduced between the transformed innovation and the latent process in an attempt to improve the statistical fits of the models. In order to perform one‐step‐ahead in‐sample and out‐of‐sample duration forecasts, an auxiliary particle filter is used to approximate the filter distributions of the latent states. Simulation studies and application to the IBM transaction dataset illustrate that our proposed estimation methods work well in terms of parameter and log duration estimation. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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