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| Newey-West HAC 标准误× | 普通最小二乘法 (OLS) 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≠ | 1987 | 2019 |
| 提出者≠ | Whitney Newey & Kenneth West | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Covariance matrix estimator | Linear regression |
| 开创性文献≠ | Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 别名 | HAC standard errors, Heteroskedasticity and Autocorrelation Consistent covariance, Bartlett kernel HAC estimator, HAC düzeltmeli standart hatalar | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关≠ | 1 | 5 |
| 摘要≠ | Newey-West HAC standard errors, introduced by Whitney Newey and Kenneth West in 1987, provide a covariance matrix estimator for OLS regression that remains valid under both heteroskedasticity and serial autocorrelation of unknown form. They are the standard tool for correcting inference in time-series and panel regression when residuals are not i.i.d., requiring no specification of the error structure beyond choosing a bandwidth parameter. | 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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