This paper enriches Pesaran (2006) and Baltagi et al. (2016) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes. Thus, an empirically appealing panel data model is provided to accommodate important features of endogeneity and structural breaks, in addition to heterogeneity and cross-sectional dependence, that prevail in applied studies. We extend the …nding of Perron and Yamamoto (2015) in a time series regression model, and use least squares to estimate common break dates even with endogenous regressors in heterogeneous panels. In addition, we show that Pesaran’s common correlated effects (CCE) approach is still valid to deal with cross-sectional dependence due to unobservable factors in the presence of endogenous regressors and structural changes in slopes and error factor loadings. Monte Carlo experiments are conducted to examine the proposed estimators in this paper.