Estimation et tests en présence d'erreurs de mesure sur les variables explicatives: vérification empirique par la méthode de simulation Monte Carlo
Bibliographic record
Abstract
In this paper we present two new estimators which are robust in the presence of errors in variables. These estimators are much less erratic than their classic counterparts: The Durbin and Pal estimators. These new estimators are based upon sample moments of order greater than two. They may be viewed as special instrumental variable estimators where the instruments are obtained by taking powers of the explanatory variables. Data from Statistics Canada on consumer finances are used to evaluate the performance of our estimators. Monte Carlo simulations show that their biases are less than those of ordinary lest squares. Our estimators may be viewed as a special case of generalized method of moments (GMM). Consequently they take part in the actual trend of research in financial econometrics. Financial econometrics might benefit greatly form our new estimators in applying them to well known models, as the CAPM where the market portfolio which is essential to the empirical verification of this model is contaminated by important measurement errors. Risk premia measures which are related to this portfolio might be corrected by our estimators, this in the context of the generalized method of moments (GMM).
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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 | 0.045 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".