Exact Simulation-Based Tests in Multivariate Regressions: Applications to Asset Pricing Models 1
Bibliographic record
Abstract
Multivariate regressions (MR) are among the simplest empirical models of finan-cial econometrics. It is well known however that despite their simple statistical structure, standard asymptotically justified MR-based tests are unreliable. Exact tests have been proposed for a few specific hypotheses [e.g. Gibbons, Ross and Shanken (Econometrica 1989), Shanken (Journal of Finance 1986), Velu and Zhou (Journal of Empirical Finance 1999), Stewart (Econometric Reviews 1997)], most of which depend on normality. In this paper, we propose likelihood based exact market-model tests for possibly non-linear hypotheses, allowing for a wide class of error distributions which include normality as a special case. The proposed test procedures are computationally attractive and may be easily obtained by simula-tion. For the Gaussian model, our test results serve to unify existing results on efficiency tests. In non-Gaussian contexts, we re-consider efficiency tests allowing for multivariate student-t errors and unknown zero-beta rate. In this case, we pro-pose a set estimate for the intervening degrees-of-freedom parameter, which serves to devise a confidence-set based exact Monte Carlo test. 1
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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 | 0.019 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".