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| المربعات الصغرى المجمعة للبيانات اللوحية× | انحدار المربعات الصغرى العادية (OLS)× | |
|---|---|---|
| المجال | الاقتصاد القياسي | الاقتصاد القياسي |
| العائلة | Regression model | Regression model |
| سنة النشأة≠ | 2010 | 2019 |
| صاحب الطريقة≠ | Jeffrey Wooldridge (treatment) | Wooldridge (textbook treatment); classical least squares |
| النوع≠ | Linear regression on stacked panel observations | Linear regression |
| المصدر التأسيسي≠ | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0-262-23258-8 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| الأسماء البديلة | Pooled OLS, Pooled Ordinary Least Squares, Simple Panel OLS, Havuzlanmış EKK | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| ذات صلة≠ | 2 | 5 |
| الملخص≠ | Pooled OLS applies standard ordinary least squares to panel data by stacking all cross-sectional and time observations into a single dataset and ignoring the panel structure during estimation. It is the most transparent starting point for panel data analysis, widely used in economics, finance, and social sciences when researchers wish to estimate average partial effects across individuals and time periods without imposing strong distributional assumptions about unobserved heterogeneity. | 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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