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রিগ্রেশন ডিসকন্টিনিউটি ডিজাইন (RDD)×ডিফারেন্স-ইন-ডিফারেন্সেস (ডিফ-ইন-ডিফ)×সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন×
ক্ষেত্রঅর্থমিতিঅর্থমিতিঅর্থমিতি
পরিবারRegression modelRegression modelRegression model
উদ্ভবের বছর200819942019
প্রবর্তকImbens & Lemieux; Lee & Lemieux (modern practice); Cattaneo, Idrobo & TitiunikCard & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)Wooldridge (textbook treatment); classical least squares
ধরনQuasi-experimental causal designCausal inference / panel regressionLinear regression
মৌলিক উৎসImbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI ↗Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
অপর নামRDD, regression discontinuity, sharp regression discontinuity, Regresyon Süreksizliği Tasarımı (RDD)diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
সম্পর্কিত555
সারসংক্ষেপRegression Discontinuity Design is a quasi-experimental method that estimates a local causal effect around a threshold (cutoff) value, comparing units just below and just above the cutoff as if they were almost randomly assigned. It is the design developed for applied practice by Imbens and Lemieux (2008) and by Lee and Lemieux (2010).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.Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).
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ScholarGateপদ্ধতির তুলনা করুন: Regression Discontinuity Design · Difference-in-Differences · OLS Regression. 2026-06-18 তারিখে সংগৃহীত, উৎস: https://scholargate.app/bn/compare