The Dynamics of Adolescent Depression: An Instrumental Variable Quantile Regression with Fixed Effects Approach
Bibliographic record
Abstract
Summary The paper employs a recently developed instrumental variable approach for the estimation of dynamic quantile regression models with fixed effects to model the dynamics of health outcomes. Our proposed estimator not only allows us to control for individual-specific heterogeneity via fixed effects in the dynamic quantile regression framework but may also reduce the bias that exists in conventional fixed effects estimation of dynamic quantile regression models with small numbers of time periods. Using data on the children of the US National Longitudinal Survey of Youth 1979 cohort, we examine the extent of true state dependence in youth depression conditional on unobserved individual heterogeneity and family socio-economic status. Our results suggest that true state dependence in youth depression among the survey respondents is very low and the observed positive association between previous and current depression is mainly due to time invariant unobserved individual heterogeneity.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".