Identification-robust inference for endogeneity parameters in linear structural models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We provide a generalization of the AndersonRubin (AR) procedure for inferenceon parameters that represent the dependence between possibly endogenous explanatoryvariables and disturbances in a linear structural equation (endogeneity parameters). We stressthe distinction between regression and covariance endogeneity parameters. Such parametershave intrinsic interest (because they measure the effect of latent variables, which inducesimultaneity) and play a central role in selecting an estimation method (such as ordinary leastsquaresor instrumental variable methods). We observe that endogeneity parameters mightnot be identifiable and we give the relevant identification conditions. These conditions entaila simple identification correspondence between regression endogeneity parameters and theusual structural parameters, while the identification of covariance endogeneity parameterstypically fails as soon as global identification fails. We develop identification-robust finitesampletests for joint hypotheses involving structural and regression endogeneity parameters,as well as marginal hypotheses on regression endogeneity parameters. For Gaussian errors,we provide tests and confidence sets based on standard Fisher critical values. For a wideclass of parametric non-Gaussian errors (possibly heavy-tailed), we show that exact MonteCarlo procedures can be applied using the statistics considered. As a special case, this resultalso holds for usual AR-type tests on structural coefficients. For covariance endogeneityparameters, we supply an asymptotic (identification-robust) distributional theory. Tests forpartial exogeneity hypotheses (for individual potentially endogenous explanatory variables)are covered as special cases. The proposed tests are applied to two empirical examples: therelation between trade and economic growth, and the widely studied problem of returns toeducation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it