方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 普通最小二乘法 (OLS) 回归× | 分位数回归× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2019 | 1978 |
| 提出者≠ | Wooldridge (textbook treatment); classical least squares | Koenker & Bassett |
| 类型≠ | Linear regression | Conditional quantile regression |
| 开创性文献≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| 别名≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | conditional quantile regression, regression quantiles, Kantil Regresyon |
| 相关 | 5 | 5 |
| 摘要≠ | 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). | 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. |
| ScholarGate数据集 ↗ |
|
|