Identification-robust Inference for Endogeneity Parameters in Models with an Incomplete Reduced Form
Bibliographic record
Abstract
Abstract The authors propose inference methods for endogeneity parameters in linear simultaneous equation models allowing for weak identification and missing instruments. Endogeneity parameters measure the impact of unobserved variables which may be correlated with observed explanatory variables, and play a central role in determining the “bias” associated with endogeneity and measurement errors in structural equations. These results expand, in several ways, the finite-sample theory in Doko Tchatoka and Dufour (2014) for this problem. The latter theory relies on relatively restrictive assumptions, in particular the hypothesis that the reduced form is complete (e.g., contains all the relevant instruments), which is questionable in many practical situations. While the new proposed inference methods retain identification robustness, they also allow the reduced form to be incomplete, for example, due to missing instruments. The authors propose easily applicable inference methods for endogeneity parameters – in particular, two-stage procedures (similar to those in Dufour, 1990). An application to a model of returns to schooling is presented.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".