Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Diferenču diskontinuitātes dizains× | Dizains ar regresijas pārtraukumu (RDD)× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 2016 | 2008 |
| Autors≠ | Grembi, Nannicini & Troiano | Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction) |
| Tips≠ | Hybrid quasi-experimental causal design (RDD + DID) | Quasi-experimental causal design |
| Pirmavots≠ | 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 ↗ | Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗ |
| Citi nosaukumi≠ | diff-in-disc, DiD-RDD, Süreksizliklerde Fark (Difference-in-Discontinuities) | RDD, regression discontinuity design, sharp RDD, fuzzy RDD |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold. |
| ScholarGateDatu kopa ↗ |
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