An Empirical study of aggregation of alternatives and its influence on prediction: case study of car type choice in Sweden
Bibliographic record
Abstract
In the car type choice models, alternatives are usually grouped into categories by some of their main characteristics such as make, model, vintage, body type and/or fuel type. Each of these categories contains different versions of the cars that are usually not recognized in the applied literature. In this study we empirically investigate whether including the heterogeneity of these versions in the modeling do matter in estimation and prediction or not. We have detailed data on alternatives available on the market down to the versions level of each model which enables us to account for heterogeneity in the model. We also have Swedish car registry data as demand. We estimate different discrete choice models with different methods of correction for alternative aggregation including nesting structure. We estimate these models on based on year 2006 Swedish registry data for new cars, predict for 2007 and compare the results. The results show that including heterogeneity of cars' versions in the model improves model fitness but it does not necessarily improve prediction results.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".