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Politikaértékelési intervallum-sorozat×A különbség-különbségek (Diff-in-Diff) módszer×
TudományterületOksági következtetésÖkonometria
MódszercsaládRegression modelRegression model
Keletkezés éve1975 (intervention analysis); 2000s–2010s (policy evaluation framing)1994
MegalkotóBox & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
TípusQuasi-experimental causal designCausal inference / panel regression
AlapműBernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alternatív nevekITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITSdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Kapcsolódó45
ÖsszefoglalóInterrupted Time Series (ITS) for policy evaluation uses routinely collected aggregate time-series data to estimate the causal impact of a policy change. A segmented regression model splits the series at a known intervention date, estimating both an immediate level shift and a change in trend attributable to the policy — without requiring a randomised control group.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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ScholarGateMódszerek összehasonlítása: Policy Evaluation Interrupted Time Series · Difference-in-Differences. Letöltve 2026-06-18, forrás: https://scholargate.app/hu/compare