Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская нечеткая регрессия разрыва (Bayesian Fuzzy Regression Discontinuity)× | Байесовский метод инструментальных переменных (Bayesian IV)× | |
|---|---|---|
| Область | Причинно-следственный вывод | Причинно-следственный вывод |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2001 (fuzzy RD identification); 2016 (Bayesian formulation by Chib & Jacobi) | 2003 |
| Автор метода≠ | Chib & Jacobi (Bayesian formulation); Hahn, Todd & Van der Klaauw (fuzzy RD identification) | Kleibergen & Zivot (2003); Lancaster (2004) |
| Тип≠ | Bayesian causal inference / quasi-experimental design | Causal inference / Bayesian estimation |
| Основополагающий источник≠ | Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209. DOI ↗ | Kleibergen, F., & Zivot, E. (2003). Bayesian and classical approaches to instrumental variable regression. Journal of Econometrics, 114(1), 29-72. DOI ↗ |
| Другие названия≠ | Bayesian Fuzzy RD, Bayesian Fuzzy RDD, Fuzzy RD with Bayesian Inference | Bayesian IV, Bayesian 2SLS, Bayesian LIML, BayesIV |
| Связанные≠ | 5 | 6 |
| Сводка≠ | Bayesian Fuzzy Regression Discontinuity (Bayesian Fuzzy RD) combines the quasi-experimental logic of fuzzy regression discontinuity design with full Bayesian inference. It estimates a local average treatment effect at a policy threshold where treatment assignment is probabilistic rather than deterministic, placing prior distributions over all unknowns and recovering a complete posterior distribution of the causal effect rather than a single point estimate. | Bayesian Instrumental Variables combines the instrumental variable strategy for addressing endogeneity with Bayesian posterior inference. Instead of relying on asymptotic sampling distributions, it places prior distributions over all structural parameters and recovers a full posterior distribution for the causal effect, providing probability statements about the parameter rather than p-values — especially valuable when instruments are weak or the sample is small. |
| ScholarGateНабор данных ↗ |
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