Mixed proportional hazard models with continuous finite mixture unobserved heterogeneity: an application to Canadian firm survival
Bibliographic record
Abstract
This paper proposes a methodology that accounts for the selection effect due to non‐random entry in duration models using latent‐class models. A mixed proportional hazard model with continuous finite mixture unobserved heterogeneity (MPH‐CFM) is introduced to correct for the potential bias induced by the selection effect. Conditions for identification, consistency, and asymptotic normality of the MPH‐CFM are provided. The estimator is used to investigate the duration of new entrant Canadian manufacturing firms. For the current application, the MPH‐CFM is compared with alternative duration models and found to be superior. Empirically, the results indicate that there are two classes of firms. Class I starts with high hazard and decreases non‐monotonically while Class II has a negligible hazard. These empirical results can be used to understand alternative models of firm dynamics. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".