Bibliographic record
Abstract
A vexing challenge when using the utility-maximization framework to estimate consumers’ decisions on which set of goods to purchase and how much quantity to buy is obtaining a functional form of the utility that satisfies three criteria: tractability, flexibility, and global regularity. Flexibility refers to the ability of a utility function to impose minimal prior restrictions on demand elasticities. Global regularity refers to the ability of a utility function to satisfy regularity properties required by economic theory in the entire feasible space of variables. The tractable utility functions used so far are either inflexible, which could yield inaccurate estimates of underlying elasticities, or do not satisfy global regularity, which can result in invalid expressions of likelihood and invalid policy simulations. I tackle this problem by deriving necessary and sufficient conditions for global regularity of Basic Translog utility. Using simulated and scanner data, I show that the proposed demand system yields better model fit, more accurately captures underlying elasticities, and yields substantially different results in counterfactuals compared to alternatives used in prior literature. Specifically, unlike the alternatives used so far, the proposed demand system allows for complementarities between goods, and more accurately captures the extent of their inferiority, the extent of their substitutability, and asymmetries in cross price effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".