Copula-based expectile regression: estimation and inference
Bibliographic record
Abstract
This article proposes a new approach to estimating the expectile regression function based on copulas. The main idea of this approach is to rewrite the expectile regression function in terms of a copula and marginal distributions. We show the asymptotic properties of our proposed estimator, for time series and iid settings, when the copula is estimated by maximizing the pseudo-log-likelihood and the margins are estimated nonparametrically. A Monte Carlo simulation study reveals that our estimator has good finite-sample properties for a variety of data-generating processes and different sample sizes. Finally, we provide two empirical applications to illustrate the practical relevance of the proposed methods. In these applications, we re-examined the relationship between volume and exchange rates on stock returns using copula-based expectile regressions. We found that the intercorrelation between two time series is a more important factor for improving the prediction than the autocorrelation in the time series.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".