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| انحدار فورييه (الانحدار المربعات الصغرى العادية المعزز بفورييه)× | انحدار المربعات الصغرى العادية (OLS)× | |
|---|---|---|
| المجال | الاقتصاد القياسي | الاقتصاد القياسي |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 2004 | 2019 |
| صاحب الطريقة≠ | Becker, Enders, and Hurn | Wooldridge (textbook treatment); classical least squares |
| النوع≠ | Augmented linear regression | Linear regression |
| المصدر التأسيسي≠ | 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 |
| الأسماء البديلة | Fourier OLS, Fourier-augmented OLS, trigonometric OLS, smooth structural break OLS | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| ذات صلة≠ | 6 | 5 |
| الملخص≠ | 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). |
| ScholarGateمجموعة البيانات ↗ |
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