Hedonic Regressions. A Consumer Theory Approach
Why this work is in the frame
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Bibliographic record
Abstract
A hedonic regression regresses the price of various models of a product (or service) on the characteristics that describe the product. The existing economic theory that justifies a hedonic regression is extremely complex. The present paper takes a very simple consumer theory approach in order to justify a family of functional forms for a hedonic regression. The main simplifying assumption is that every consumer has the same hedonic utility function, which describes how consumers evaluate alternative models with different characteristics. This hedonic utility function is assumed to be separable from other goods, which is the second main simplifying assumption. The paper also examines alternative functional forms for the hedonic utility function from the viewpoint of their flexibility properties; i.e., how well they can approximate arbitrary functional forms. The paper notes that hedonic regressions that regress the model price on a linear function of the characteristics is not consistent with the consumer approach adopted in the paper. Finally, the paper compares traditional statistical agency matched model techniques for dealing with quality change with the hedonic regression approach and indicates under what conditions the two approaches are likely to coincide.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it