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| Дизайн на разлики в дисконтинуитети× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Причинно-следствено заключение | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2016 | 2019 |
| Създател≠ | Grembi, Nannicini & Troiano | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Hybrid quasi-experimental causal design (RDD + DID) | Linear regression |
| Основополагащ източник≠ | Grembi, V., Nannicini, T. & Troiano, U. (2016). Do Fiscal Rules Matter? A Difference-in-Discontinuities Design. American Economic Journal: Applied Economics, 8(3), 1-30. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Други названия≠ | diff-in-disc, DiD-RDD, Süreksizliklerde Fark (Difference-in-Discontinuities) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 |
| Резюме≠ | Difference-in-Discontinuities is a hybrid quasi-experimental design that fuses regression discontinuity (RDD) with difference-in-differences (DID), introduced by Grembi, Nannicini and Troiano (2016). It compares the discontinuity at the same cutoff value across two periods to isolate a causal effect. | 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). |
| ScholarGateНабор от данни ↗ |
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