Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель цензурованої регресії Тобіта× | Регресія негативним біноміальним розподілом× | Регресія звичайно найменших квадратів (ЗНК)× | |
|---|---|---|---|
| Галузь | Економетрика | Економетрика | Економетрика |
| Родина | Regression model | Regression model | Regression model |
| Рік появи≠ | 1958 | 2011 | 2019 |
| Автор методу≠ | James Tobin | Hilbe (textbook treatment); generalized linear model framework | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Censored regression (limited dependent variable) | Generalized linear model for count data | Linear regression |
| Основоположне джерело≠ | 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 ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Інші назви≠ | censored regression, limited dependent variable model, Tobit Modeli (Sansürlü Regresyon) | NB regression, NB2 regression, negatif binom regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Пов'язані≠ | 4 | 4 | 5 |
| Підсумок≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateНабір даних ↗ |
|
|
|