THE LINEAR APROXIMATION OF THE ALMOST IDEAL DEMAND SYSTEM (LA/AIDS) MODEL: APPLICATION TO AGGREGATED DATA
Bibliographic record
Abstract
During the years different models for demand system analysis were developed, but one formulation that avoids many shortcomings encountered in other models is the Almost Ideal Demand System (AIDS) model, developed by A. Deaton and J. Muellbauer in 1980. Several variations of AIDS were used in applied work since 1980 (Linear Approximation of AIDS, Dynamic AIDS, etc). This paper is an application of the Linear Approximate Almost Ideal Demand System (LA/AIDS) model on an aggregated demand system. The data used for this analysis are from a database collected by ACNielsen Co. on sliced packaged luncheon meats, for Canada, during December 2000 - September 2006. Because the data are very detailed some level of aggreg ation needs to be applied. The results will illustrate th at not all the theoretical assumptions of the deman d theory hold in the case of aggregation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".