Bibliographic record
Abstract
provided by Xifeng Diao. We are very appreciative of financial support provided for this project by the Social Sciences and Humanities Research Council of Canada under grant 410-2002-0641. We propose a discrete-time stochastic volatility model in which regime-switching serves three purposes. First, it captures low frequency vari-ations, which is its traditional role. Second, it specifies intermediate frequency dynamics that are usually assigned to smooth autoregressive processes. Finally, it generates a fat-tailed conditional distribution of returns. A single mechanism thus captures three important features of the data that are typically addressed as distinct phenomena in the literature. Maximum likelihood estimation is developed and shown to perform well in typical sample sizes. We also construct a simu-lated method of moments (SMM) estimator in which scaling statis-tics, log-covariagrams, log-periodograms, and tail statistics are used as potential identifying restrictions. We estimate on exchange rate data a process with four parameters and more than a thousand states. The estimated process performs well both in-sample and out-of-sample when compared with previous models. The paper thus contributes to the regime-switching literature by offering an effective technique for parsimoniously specifying high-dimensional state spaces. We also ex-tend the emerging multifractal literature by proposing a convenient time series construction and by developing an econometric toolkit of estimation and testing methods. JEL Classification: G0, C5.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.652 | 0.376 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".