Markov-switching quantile autoregression: a Gibbs sampling approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We extend the class of linear quantile autoregression models by allowing for the possibility of Markov-switching regimes in the conditional distribution of the response variable. We also develop a Gibbs sampling approach for posterior inference by using data augmentation and a location-scale mixture representation of the asymmetric Laplace distribution. Bayesian calculations are easily implemented, because all complete conditional densities used in the Gibbs sampler have closed-form expressions. The proposed Gibbs sampler provides the basis for a stepwise re-estimation procedure that ensures non-crossing quantiles. Monte Carlo experiments and an empirical application to the U.S. real interest rate show that both inference and forecasting are improved when the quantile monotonicity restriction is taken into account.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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