方法对比
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| 傅里叶广义最小二乘法 (Fourier Generalized Least Squares)× | 广义最小二乘法 (GLS)× | |
|---|---|---|
| 领域≠ | 计量经济学 | 统计学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2004-2012 | 1935 |
| 提出者≠ | Becker, Enders, and Hurn; extended by Enders and Lee | Alexander Craig Aitken |
| 类型≠ | Time-series regression estimator | Linear estimator |
| 开创性文献≠ | Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899-906. DOI ↗ | Aitken, A. C. (1935). IV.—On least squares and linear combination of observations. Proceedings of the Royal Society of Edinburgh, 55, 42–48. DOI ↗ |
| 别名≠ | Fourier GLS, Fourier-based GLS, Fourier flexible GLS, spectral GLS | GLS, Aitken estimator, EGLS, feasible GLS |
| 相关≠ | 1 | 3 |
| 摘要≠ | Fourier GLS embeds low-frequency trigonometric (Fourier) terms into a generalized least squares framework to capture smooth, gradual structural change in a time series without requiring the researcher to specify when or how many breaks occurred. The approach is particularly valued in unit root testing and cointegration analysis where conventional break-date assumptions may be arbitrary. | Generalized Least Squares (GLS) is a linear regression estimator that extends ordinary least squares to handle situations where the error terms are correlated or have non-constant variance (heteroscedasticity). Introduced by Alexander Craig Aitken in 1935, GLS achieves the Best Linear Unbiased Estimator (BLUE) under a general error covariance structure by weighting observations according to their precision, providing a theoretical bridge between OLS and modern linear mixed models. |
| ScholarGate数据集 ↗ |
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