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Robust Regression Discontinuity Design (मजबूत रिग्रेशन डिसकंटीन्यूइटी डिज़ाइन)×अंतर-में-अंतर (डिफ-इन-डिफ)×
क्षेत्रकारणात्मक अनुमानअर्थमिति
परिवारRegression modelRegression model
उद्भव वर्ष20141994
प्रवर्तकCalonico, Cattaneo & TitiunikCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
प्रकारQuasi-experimental causal inferenceCausal inference / panel regression
मौलिक स्रोतCalonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
उपनामRobust RDD, Bias-corrected RDD, CCT estimator, rdrobustdiff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
संबंधित45
सारांशRobust RDD extends the classical regression discontinuity design with bias correction and robust confidence intervals, addressing the under-coverage problem of conventional RDD inference. Developed by Calonico, Cattaneo, and Titiunik (2014), it uses local polynomial estimation with a bias-corrected point estimate and a wider variance term that accounts for the added uncertainty, yielding confidence intervals with correct asymptotic coverage.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.
ScholarGateडेटासेट
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  2. 2 स्रोत
  3. PUBLISHED

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