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。
| 块自举（移动块和固定块）× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1989 | 2019 |
| 提出者≠ | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Resampling inference for dependent data | Linear regression |
| 开创性文献≠ | Künsch, H. R. (1989). The Jackknife and the Bootstrap for General Stationary Observations. Annals of Statistics, 17(3), 1217-1241. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名≠ | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关 | 5 | 5 |
| 摘要≠ | Block bootstrap is a resampling method for dependent, autocorrelated time-series data: instead of resampling single observations, it resamples whole blocks of consecutive observations so the serial-correlation structure is preserved. The moving block variant was introduced by Künsch (1989) and the stationary variant by Politis and Romano (1994). | 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). |
| ScholarGate数据集 ↗ |
|
|