Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Politikas novērtējums ar "starpību starp starpībām" metodi× | Dinamiskā "starpību starpībās" metode× | |
|---|---|---|
| Nozare | Cēloņsakarību secināšana | Cēloņsakarību secināšana |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1978-2009 | 2021 |
| Autors≠ | Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009) | Callaway & Sant'Anna; Sun & Abraham |
| Tips≠ | Quasi-experimental / policy evaluation | Causal inference / quasi-experimental |
| Pirmavots≠ | Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86. DOI ↗ | Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230. DOI ↗ |
| Citi nosaukumi | policy DiD, program evaluation DiD, policy impact DiD, DiD policy assessment | Dynamic DiD, Staggered DiD, Event-time DiD, Heterogeneous-timing DiD |
| Saistītās | 4 | 4 |
| Kopsavilkums≠ | Policy Evaluation DiD applies the difference-in-differences estimator specifically to assess the causal impact of government programs, regulations, or policy reforms. It compares outcome changes in a group exposed to the policy against a comparable untreated group, before and after the policy took effect, isolating the net policy effect from pre-existing trends and time-common shocks. | Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time. |
| ScholarGateDatu kopa ↗ |
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