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| 블록 부트스트랩 (이동 블록 및 정상성)× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야≠ | 통계학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1989 | 2019 |
| 창시자≠ | Künsch (moving block, 1989); Politis & Romano (stationary, 1994) | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Resampling inference for dependent data | Linear regression |
| 원전≠ | 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 |
| 별칭≠ | moving block bootstrap, stationary bootstrap, blok bootstrap (moving block / stationary) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련 | 5 | 5 |
| 요약≠ | 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). |
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