The Contribution of Spatial Econometrics in the Field of Empirical Finance
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
Spatial econometrics is a subset of econometric methods evolved from the need to account for the location and spatial interaction. This means that what happens in one economic unit of analysis is not independent of what happens in neighboring economic units. Spatial econometric methods have been advanced quickly and many studies show the usefulness of these techniques in various fields. However, they have not yet received sufficient attention in empirical finance. So, this article asks the question: what should a financier who wishes to use regression models involving spatial data know about spatial econometric methods? More precisely, this paper has two goals. In the one hand, it attempts to present a review of the peculiarities of spatial econometrics, and, in the other hand, it discusses the application of spatial econometrics in the field of finance. It summarizes some of the different spatial econometrics models that have been used in finance, and describes different kind of economic and financial distance.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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