Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Furjē OLS (Furjē papildinātais parastais mazāko kvadrātu metodes)× | Nelineārā OLS (Nelineārā mazāko kvadrātu summa)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2004 | 1974–1987 |
| Autors≠ | Becker, Enders, and Hurn | Gallant (1987); Wooldridge (2010) for econometric treatment |
| Tips≠ | Augmented linear regression | Nonlinear regression estimator |
| Pirmavots≠ | 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 |
| Citi nosaukumi | Fourier OLS, Fourier-augmented OLS, trigonometric OLS, smooth structural break OLS | nonlinear least squares, NLS, NLLS, nonlinear regression |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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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