Сравнение на методи

	OLS с отчитане на структурни прекъсвания	Метод на най-малките квадрати (МНК)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1960–1998	2019
Създател	Chow (1960) for the breakpoint test; Bai & Perron (1998) for multiple break estimation	Wooldridge (textbook treatment); classical least squares
Тип	Segmented linear regression	Linear regression
Основополагащ източник	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Други названия	OLS with structural breaks, piecewise OLS, regime-switching OLS, breakpoint regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Свързани	6	5
Резюме	Structural Break OLS extends ordinary least squares to allow regression coefficients to shift at one or more breakpoints in time or across regimes. Rather than forcing a single coefficient vector across the entire sample, the model partitions the data and estimates a separate OLS regression within each segment, making it appropriate when economic relationships are suspected to change due to policy shifts, crises, or other structural events.	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).