Methoden vergelijken

	Structurele Breuk Random Effect Model	Analyse van structurele breuken in paneeldata
Vakgebied	Econometrie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	1998–2000s	1998-2010
Grondlegger	Bai & Perron (break detection); Baltagi (panel RE framework)	Bai & Perron (1998); extended to panels by Bai (2010) and Joseph et al.
Type	Panel regression with regime shifts	Panel time-series model with regime shifts
Oorspronkelijke bron	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47–78.	Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica, 66(1), 47-78.
Aliassen	RE model with structural breaks, break-adjusted random effects, random effects break model, panel RE with regime shifts	panel structural break test, break-point panel model, panel change-point analysis, regime-shift panel analysis
Verwant	5	4
Samenvatting	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.	Structural break panel data analysis detects and estimates points in time — break dates — where the underlying regression coefficients shift permanently across a panel of cross-sectional units observed over multiple periods. By jointly exploiting cross-sectional and time-series variation, it offers sharper identification of regime shifts than single-series break tests, and it delivers separate coefficient estimates for each regime before and after each break.