Сравнение методов

	Модель случайных эффектов со структурными разрывами	Модель случайных эффектов для панельных данных
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1998–2000s	1966
Автор метода	Bai & Perron (break detection); Baltagi (panel RE framework)	Balestra & Nerlove
Тип	Panel regression with regime shifts	Panel data estimator
Основополагающий источник	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	Balestra, P., & Nerlove, M. (1966). Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica, 34(3), 585–612.
Другие названия	RE model with structural breaks, break-adjusted random effects, random effects break model, panel RE with regime shifts	random effects estimator, RE model, GLS random effects, error components model
Связанные	5	5
Сводка	The structural break random effects model extends standard panel RE estimation by allowing one or more breakpoints at which slope coefficients or error variances shift across time. It combines structural change detection (e.g., Bai-Perron) with the GLS-based random effects estimator, producing regime-specific parameter estimates while retaining the efficiency gains of pooling individual-level variation as random draws from a common distribution.	The panel random effects (RE) model treats individual-specific effects as random draws from a population distribution rather than fixed constants, enabling efficient estimation by generalised least squares and allowing inference about time-invariant regressors that are swept away in fixed effects estimation.