Heteroskedasticity and Spatiotemporal Dependence Robust Inference for Linear Panel Models with Fixed Effects
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
This paper studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator, which is flexible to nest existing estimators as special cases with certain choices of bandwidths. For distributional approximations, we consider two different types of asymptotics. When the level of smoothing is assumed to increase with the sample size, the proposed estimator is consistent and the associated Wald statistic converges to a χ2 distribution. We show that our covariance estimator improves upon existing estimators in terms of robustness and efficiency. When we assume the level of smoothing to be held fixed, the covariance estimator has a random limit and we show by asymptotic expansion that the limiting distribution of the test statistic depends on the bandwidth parameters, the kernel function, and the number of restrictions being tested. As this distribution is nonstandard, we establish the validity of an F-approximation to this distribution, which greatly facilitates the test. For optimal bandwidth selection, we propose a procedure based on the upper bound of asymptotic mean square error criterion. The flexibility of our estimator and proposed bandwidth selection procedure make our estimator adaptive to the dependence structure in data. This adaptiveness automates the selection of covariance estimator. That is, our estimator reduces to the existing estimators which are designed to cope with the particular dependence structures. Simulation results show that the F-approximation and the adaptiveness work reasonably well.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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