方法对比
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| 离散选择模拟× | 多项逻辑回归× | |
|---|---|---|
| 领域≠ | 仿真 | 计量经济学 |
| 方法族≠ | Process / pipeline | Regression model |
| 起源年份≠ | 1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s | 1974 |
| 提出者≠ | Daniel McFadden (random utility theory); Kenneth Train (simulation methods) | McFadden |
| 类型≠ | Discrete choice modelling with Monte Carlo simulation | Multinomial logistic regression |
| 开创性文献≠ | Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. DOI ↗ | McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503 |
| 别名 | stated preference simulation, SP simulation, revealed preference modelling, Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation) | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon |
| 相关 | 5 | 5 |
| 摘要≠ | Discrete choice simulation is a behavioural modelling method — grounded in random utility theory formalised by Daniel McFadden in the 1970s and extended to simulation-based estimation by Kenneth Train — that estimates how individuals choose among mutually exclusive alternatives and then uses those estimated preference parameters to forecast how choice shares would shift under hypothetical policy or market scenarios. It is the dominant quantitative tool in transport demand analysis, health economics, environmental valuation, and marketing research. | Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category. |
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