Bibliographic record
Abstract
This dissertation generalizes the asymptotic theory of dynamic cross-section heterogeneous coefficient panels with interactive fixed effects to allow for the cross-section units to be correlated due to local shocks. Traditional heterogeneous coefficient estimators in the multifactor error structure literature typically only focus on the influence that global shocks have on model estimates; global shocks such as the impact of the COVID-19 pandemic on a panel of world economic growth rates. We extend this focus to also allow for local shocks that significantly impact only a small subset of the cross-section units in the sample. Local shocks like the impact of a falling crop price on the significant producer countries of that crop or the impact of a drought on the drought-affected nations. We derive the limiting distribution for the cross-section heterogeneous coefficients under √TN → c, 0 ⩽ c < ∞ asymptotics (where N and T are the number of cross-sections and time periods respectively). We observe a bias in the coefficient estimates associated with local shocks to the dependent variable (i.e., associated with the weak cross-section dependence of the idiosyncratic error). We provide sufficient conditions so that the bias and the covariance matrix of the limiting distribution can be consistently estimated in the presence of local and global shocks. Our theoretical findings are accompanied by extensive Monte Carlo experiments demonstrating the often superior finite sample performance of our estimation method over other competing techniques when the idiosyncratic errors are weakly cross-section dependent. We also provide an empirical application of our estimator and evaluate the country-specific long-run impact public debt has on economic growth for 86 countries.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".