On the Treatment of Heteroscedasticity in Crop Yield Data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In empirical applications with crop yield data, conditioning for heteroscedasticity is both important and challenging. It is important because the scale of the distribution can markedly influence the results, and challenging because statistical tests for the common heteroscedasticity assumptions (constant or proportional variance) often lead to ambiguous conclusions. Alternatively, Harri et al. (2011) proposed a methodology that estimates the degree of heteroscedasticity, removing the need to make a specific assumption. Such approaches assume that volatility changes are symmetric (identical) across tails of the yield distribution. We propose a generalization to the Harri et al. (2011) methodology, which allows asymmetry between the tails, akin to the generalization of GARCH to AGARCH. Using U.S. county level yield data from 1951–2017, we find evidence of asymmetry in corn and soybean, but not wheat. Moreover, the asymmetry takes a particular form—increasing volatility in the lower tail. To investigate economic significance, we consider the effect of imposing symmetric heteroscedasticity in rating crop insurance contracts, as currently done by the USDA's Risk Management Agency in rating their Area Risk Protection products. We find that relaxing the symmetry assumption leads to economically and statistically significant rents. Our results suggest that the Risk Management Agency and others should consider the possibly asymmetric nature of heteroscedasticity in crop yield data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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