Partially linear models with endogeneity: a conditional moment-based approach
Why this work is in the frame
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Bibliographic record
Abstract
Summary In a partially linear conditional moment model we propose a new estimator for the slope parameter of the endogenous variable of interest, which combines a Robinson’s transformation to partial out the nonlinear part of the model, with a smooth minimum distance approach to exploit all the information of the conditional mean independence restriction. Our estimator only depends on one tuning parameter, is easy to compute, consistent and $\sqrt{n}$-asymptotically normal under standard regularity conditions. Simulations show that our estimator is competitive with the generalised method of moments-type estimators and often displays a smaller bias and variance as well as better coverage rates for confidence intervals. We revisit and extend some of the empirical results in Dinkelman (2011b) who estimates the impact of electrification on employment growth in South Africa. Overall, we obtain estimates that are smaller in magnitude, more precise, and still economically relevant.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it