方法对比
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| 傅里叶普通最小二乘法（傅里叶增强普通最小二乘法）× | 非线性OLS（非线性最小二乘法）× | |
|---|---|---|
| 领域 | 计量经济学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2004 | 1974–1987 |
| 提出者≠ | Becker, Enders, and Hurn | Gallant (1987); Wooldridge (2010) for econometric treatment |
| 类型≠ | Augmented linear regression | Nonlinear regression 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 ↗ | Gallant, A. R. (1987). Nonlinear Statistical Models. John Wiley & Sons. ISBN: 978-0471802600 |
| 别名 | Fourier OLS, Fourier-augmented OLS, trigonometric OLS, smooth structural break OLS | nonlinear least squares, NLS, NLLS, nonlinear regression |
| 相关≠ | 6 | 5 |
| 摘要≠ | Fourier OLS is an OLS regression extended by adding low-frequency trigonometric (sine and cosine) terms to the regressor matrix. These Fourier components approximate smooth, gradual structural changes in the regression relationship over time without requiring knowledge of the number, timing, or form of the breaks. | Nonlinear Ordinary Least Squares (NLS) estimates regression models in which the conditional mean function is nonlinear in the parameters. Like standard OLS it minimises the sum of squared residuals, but because no closed-form solution exists the estimator is found by iterative numerical optimisation. Under standard regularity conditions NLS is consistent and asymptotically normal. |
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