The Multiple Discrete-Continuous Extreme Value Model (MDCEV) with fixed costs
Bibliographic record
Abstract
In this paper, we present a model that can be viewed as an extension of the traditional Tobit model. As\nopposed to that specific model, ours also accounts for the the fixed costs of car ownership. That extension is\nneeded since being carless is an option for many households in societies that have a good system of public\ntransportation, the main reason being that carless households wish to save the fixed costs of car ownership.\nSo far, no existing model can adequately map the impact of these fixed costs on car ownership. The Multiple\nDiscrete-Continuous Extreme Value Model (MDCEV) with fixed costs fills this gap. In fact, this model can\nevaluate the effect of policies intended to influence household behaviour with respect to car ownership,\nwhich can be of great interest to policy makers. Our model makes it possible to compute the effect of\npolicies such as taxes on fuel or on car ownership on both the share of carless households and the average\ndriving distance.\nWe calibrated the model using data on Swiss private households in order to forecast were then able to\nforecast responses to policies. One result of particular interest that cannot be produced by other models is the\nevaluation of the impact of a tax on car ownership. Our results show that a tax on car ownership has a much\nlower impact on aggregate driving demand – per unit of tax revenues – than a tax on fuel.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".