Partial Identification and Inference for Dynamic Models and Counterfactuals
Bibliographic record
Abstract
We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures.In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and low-dimensional outcomes of interest, such as the average welfare effects of hypothetical policy interventions.We char-acterize the properties of the sets analytically and show that when the target outcome of interest is a scalar, its identified set is an interval whose endpoints can be calculated by solving well-behaved constrained optimization problems via standard algorithms.We obtain a uniformly valid inference pro-cedure by an appropriate application of subsampling.To illustrate the performance and computational feasibility of the method, we consider both a Monte Carlo study of firm entry/exit, and an empirical model of export decisions applied to plant-level data from Colombian manufacturing industries.In these applications, we demonstrate how the identified sets shrink as we incorporate alternative model restrictions, providing intuition regarding the source and strength of identification.
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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 | 0.066 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".