השוואת שיטות
סקרו את השיטות שבחרתם זו לצד זו; שורות שבהן יש הבדל מודגשות.
| אומד הריבועים הפחותים הרגילים הדינמי (Dynamic Ordinary Least Squares - DOLS)× | רגרסיית ריבועים פחותים רגילים (OLS)× | |
|---|---|---|
| תחום | אקונומטריקה | אקונומטריקה |
| משפחה | Regression model | Regression model |
| שנת המקור≠ | 1993 | 2019 |
| הוגה השיטה≠ | Stock & Watson (1993); panel extension Kao & Chiang (2001) | Wooldridge (textbook treatment); classical least squares |
| סוג≠ | Cointegrating regression estimator | Linear regression |
| מקור מכונן≠ | Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| כינויים | DOLS, Stock-Watson dynamic OLS, dynamic least squares cointegration estimator, Dinamik OLS (DOLS) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| קשורות | 5 | 5 |
| תקציר≠ | Dynamic OLS is a cointegrating-regression estimator introduced by Stock and Watson (1993) that recovers the long-run relationship between I(1) variables. It augments the static regression with leads and lags of the differenced regressors, correcting endogeneity bias parametrically so that the long-run coefficient can be estimated by ordinary least squares. | 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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