A Machine Learning Approach to Stochastic Frontier Modeling
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
<title>Abstract</title> We propose a two-stage stochastic frontier model that can handle complex non-linear patterns. In the first stage, we apply a panel data neural network to predict the demeaned composed error term. In the second stage, we apply traditional Stochastic Frontier Analysis to the residuals to obtain efficiency estimates. To illustrate our methodology, we employ quarterly data to estimate the technical efficiencies of large US banks from the first quarter of 1984 to the second quarter of 2010. The mean efficiency of US banks during this time period is 93.97%. The second quarter of 2004 through the fourth quarter of 2008, the median efficiencies of these banks are significantly lower than the overall average, with an average of 87.86%. This is in line with the financial conditions experienced during this time period. <italic> <bold>JEL Classification</bold> </italic> <bold>:</bold> C23, C45, D24, G21.
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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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| 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