方法对比
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| Tobit删失回归模型× | 负二项回归× | 分位数回归× | |
|---|---|---|---|
| 领域 | 计量经济学 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model | Regression model |
| 起源年份≠ | 1958 | 2011 | 1978 |
| 提出者≠ | James Tobin | Hilbe (textbook treatment); generalized linear model framework | Koenker & Bassett |
| 类型≠ | Censored regression (limited dependent variable) | Generalized linear model for count data | Conditional quantile regression |
| 开创性文献≠ | Tobin, J. (1958). Estimation of Relationships for Limited Dependent Variables. Econometrica, 26(1), 24-36. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 别名 | censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon) | NB regression, NB2 regression, negatif binom regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 相关≠ | 4 | 4 | 5 |
| 摘要≠ | The Tobit model is a regression for outcomes that are censored at a threshold, estimating the relationship by maximum likelihood. Introduced by James Tobin in 1958, it addresses the pile-up of observations at a limit (typically zero) in data such as spending, wages, or duration. | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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