Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Estimator OLS Complet Modificat (FMOLS)× | Regresia prin metoda celor mai mici pătrate ordinare (OLS)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1990 | 2019 |
| Autorul original≠ | Phillips & Hansen (time series); Pedroni (heterogeneous panels) | Wooldridge (textbook treatment); classical least squares |
| Tip≠ | Cointegrating regression estimator | Linear regression |
| Sursa seminală≠ | Phillips, P. C. B. & Hansen, B. E. (1990). Statistical Inference in Instrumental Variables Regression with I(1) Processes. Review of Economic Studies, 57(1), 99–125. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Denumiri alternative≠ | fully modified OLS, Phillips-Hansen FMOLS, Tam Düzeltilmiş OLS (FMOLS) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Înrudite | 5 | 5 |
| Rezumat≠ | Fully Modified OLS, introduced by Phillips and Hansen (1990), estimates the long-run coefficients of a cointegrating relationship among I(1) variables. It applies a semi-parametric correction to ordinary least squares to remove the bias that endogeneity and serial correlation otherwise induce in cointegrated time series or panel data. | 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). |
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