Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| OLS ya Fourier (Fourier-Augmented Ordinary Least Squares)× | Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)× | |
|---|---|---|
| Nyanja | Ekonometriki | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2004 | 2019 |
| Mwanzilishi≠ | Becker, Enders, and Hurn | Wooldridge (textbook treatment); classical least squares |
| Aina≠ | Augmented linear regression | Linear regression |
| Chanzo asilia≠ | Becker, R., Enders, W., & Hurn, S. (2004). A general test for time dependence in parameters. Journal of Applied Econometrics, 19(7), 899–906. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Majina mbadala | Fourier OLS, Fourier-augmented OLS, trigonometric OLS, smooth structural break OLS | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | 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. | 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). |
| ScholarGateSeti ya data ↗ |
|
|