Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| OLS-regressio (Ordinary Least Squares)× | Paneeli yleistetty pienimmän neliösumman menetelmä (Paneeli GLS)× | |
|---|---|---|
| Tieteenala | Ekonometria | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2019 | 1935 / developed for panels 1980s–1990s |
| Kehittäjä≠ | Wooldridge (textbook treatment); classical least squares | Aitken (1935); extended to panel data by Baltagi and others |
| Tyyppi≠ | Linear regression | Generalized linear regression |
| Alkuperäislähde≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). MIT Press. ISBN: 978-0262232586 |
| Rinnakkaisnimet | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | Panel GLS, Generalized Least Squares for panel data, FGLS panel, feasible GLS panel |
| Liittyvät≠ | 5 | 3 |
| Tiivistelmä≠ | 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). | Panel GLS is a regression method for longitudinal data that explicitly models the non-spherical error structure — heteroscedasticity across units and serial correlation within units — to recover efficient coefficient estimates. Unlike OLS, it weights observations by the inverse of the error covariance matrix, yielding the Best Linear Unbiased Estimator when the error structure is correctly specified. |
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