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| 修正済みOLS（FMOLS）推定量× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1990 | 2019 |
| 提唱者≠ | Phillips & Hansen (time series); Pedroni (heterogeneous panels) | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Cointegrating regression estimator | Linear regression |
| 原典≠ | 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 |
| 別名≠ | fully modified OLS, Phillips-Hansen FMOLS, Tam Düzeltilmiş OLS (FMOLS) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連 | 5 | 5 |
| 概要≠ | 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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