The rank and model specification of demand systems: an empirical analysis using United States microdata
Bibliographic record
Abstract
A rank‐three demand system is estimated with United States Consumer Expenditure Survey microdata. A unique price data set is also used, which permits the analysis of effects of systematic errors in price variables. It is found that errors in price variables bias test results for the rank‐three hypothesis, in particular towards rejection. Other test results are affected to a lesser extent. Estimating smaller systems of demand equations, even when conditioning on excluded goods, yields significantly different results. Another important conclusion is that model specification is statistically significantly different for households of varying family sizes and housing tenure statuses. JEL Classifications: C31, D12. Le rang et la spécification du modèle des systèmes de demande: une analyse empirique utilisant des microdonnées américaines. On calibre un système de demande de rang trois à l'aide de microdonnées américaines tirées de la United States Consumer Expenditures Survey. Un ensemble unique de prix est utilisé afin de permettre l'analyse des effets d'erreurs systématiques dans les variables de prix. Il appert que les erreurs dans les variables de prix distorsionnent les résultats du test de l'hypothèse de rang trois en faveur d'un rejet. D'autres résultats de tests sont affectés à un moindre degré. Si l'on calibre de plus petits systèmes d'équations de demande, même en posant des conditions sur des biens exclus, des résultats qui diffèrent de manière significative s'ensuivent. Une autre conclusion importante est que la spécification du modèle est différente de manière statistiquement significative pour les ménages selon la taille de la famille et le statut domiciliaire.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".