Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mtafsiri wa Makadirio ya Mechi ya Tathmini ya Sera× | Tofauti-katika-Tofauti (Diff-in-Diff)× | |
|---|---|---|
| Nyanja≠ | Uhitimisho wa Kisababishi | Ekonometriki |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 1998-2006 | 1994 |
| Mwanzilishi≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment) |
| Aina≠ | Non-parametric causal estimator | Causal inference / panel regression |
| Chanzo asilia≠ | Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267. DOI ↗ | Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| Majina mbadala≠ | matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator | diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff) |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation. | Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes. |
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