Сравнение методов

	Регрессия методом обыкновенных наименьших квадратов (ОНМК)	Регрессия Лассо	Модель с фиксированными эффектами для панельных данных
Область	Эконометрика	Машинное обучение	Эконометрика
Семейство	Regression model	Machine learning	Regression model
Год появления	2019	1996	2014
Автор метода	Wooldridge (textbook treatment); classical least squares	Tibshirani, R.	Hsiao (textbook treatment); within transformation of panel data
Тип	Linear regression	Regularized linear regression (L1 penalty)	Panel data regression
Основополагающий источник	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Другие названия	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Связанные	5	4	5
Сводка	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).	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).