Abstract
SummaryThis paper extends Pesaran's (Econometrica, 2006, 74, 967–1012) common correlated effects (CCE) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure. Since endogenous regressors and structural breaks are often encountered in empirical studies with large panels, this extension makes Pesaran's CCE approach empirically more appealing. In addition to allowing for slope heterogeneity and cross‐sectional dependence, we find that Pesaran's CCE approach is also valid when dealing with unobservable factors in the presence of endogenous regressors and structural changes in slopes and error factor loadings. This is supported by Monte Carlo experiments.
Highlights
This paper extends Pesaran (2006) and Baltagi, Feng and Kao (2016) (BFK hereafter) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure
Hall, Han and Boldea (2012) use instrumental variable (IV) estimation and show that break fractions and slopes can be consistently estimated in a time series setup
This paper extends Pesaran (2006) and BFK by allowing for endogenous regressors and unknown common structural changes in slopes and error factor loadings in large heterogeneous panels
Summary
This paper extends Pesaran (2006) and Baltagi, Feng and Kao (2016) (BFK hereafter) by allowing for endogenous regressors in large heterogeneous panels with unknown common structural changes in slopes and error factor structure. As indicated in the Monte Carlo experiments in the online Appendix, a break in the error factor loadings could lead to a spurious break in slope parameters Such error factor structure instability could a¤ect the estimation procedure proposed in similar panel change point models like Li, Qian, Su (2016) in which Bai’s (2009) interactive ...xed e¤ects approach is used to deal with cross-sectional dependence in the errors. There are two sources of endogeneity in xit in this model: one is due to common factors ft, and the other one is due to Cov("it; vit) 6= 0 In this way, this model accommodates 4 important empirical features: slope heterogeneity, cross-sectional dependence, structural breaks and endogeneity. The parameters of interest are cross-sectional averages of the slopes 1i and 2i, and the common break k0
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
