Сравнение на методи

	Метод на най-малките квадрати (МНК)	Регресионен дизайн с прекъсване (Regression Discontinuity Design - RDD)
Област	Иконометрия	Причинно-следствено заключение
Семейство	Regression model	Regression model
Година на възникване	2019	2008
Създател	Wooldridge (textbook treatment); classical least squares	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Тип	Linear regression	Quasi-experimental causal design
Основополагащ източник	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Други названия	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Свързани	5	5
Резюме	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).	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.