Nonparametric instrumental variable models
Bibliographic record
Abstract
Instrumental variables are widely used in applied statistics and econometrics to achieve identification and carry out inference in models that contain endogenous explanatory variables.In the usual setup the function of interest is assumed to be known up to finitely many unknown parameters and instrumental variables aid in identification of these parameters.However, this is a strong assumption that is rarely justified by economic theory and so nonparametric methods provide a more flexible alternative to model endogenous data in the sense no assumptions on the parametric form of a function are required.In this thesis we first examine the role of a single instrumental variable to achieve identification in a linear model through the stronger conditional moment restriction assumption that is usually imposed in the nonparametric framework.We do this by approximating the conditional moment restriction by an increasing sequence of moment restrictions that correspond to discretizing/binning the instrumental variable.Finally, we examine the nonparametric instrumental variable model when the explanatory variable has been discretized to provide a growing approximation of the unknown function and the instrumental variable has been discretized to approximate the conditional moment restriction.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".