Bibliographic record
Abstract
This thesis collects three essays, each in the form of a chapter, on two independent topics in econometrics. The first two chapters address the problem of uniformly valid inference for set-identified linear functionals in models with linear moment inequalities. The identified set for the linear functional is characterized by value functions of two linear programs parameterized by the underlying probability distribution which generates the data. Chapter 1 exploits the generic nature of well-behaved linear programs to show that a randomly perturbed bootstrap-based confidence set delivers a computationally tractable inference procedure that is uniformly asymptotically valid over a large class of data-generating processes. Importantly, it is established that the validity of the method does not rely on high-level assumptions that are difficult to verify in practice. In Chapter 2, in a subclass of linear moment inequality models, an alternative method is developed to obtain a never-empty confidence set with similar properties. This is motivated by the fact that many moment inequality models have a large number of moment constraints relative to the dimension of the structural parameters, in which case confidence sets based on an optimization framework are often empty. Chapter 3 considers an independent problem of identification and estimation in measurement error models. Unlike in a classical measurement error setting where independent measurements are observed, economic data is often truncated by ranking and thus necessarily correlated. A notable example is data on ascending auctions with independent private values where only drop-out bids are observed. This raises the question of whether the underlying data-generating process is nonparametrically identified when the ranks of the order statistics are known. It is shown that the model is identified under some tail conditions on the distribution of measurement errors and a uniformly consistent nonparametric estimator is proposed.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".