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Full-Text Articles in Social and Behavioral Sciences
Estimation Of Heterogeneous Panels With Structural Breaks, Badi Baltagi
Estimation Of Heterogeneous Panels With Structural Breaks, Badi Baltagi
Center for Policy Research
This paper extends Pesaran's (2006) work on common correlated effects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that includes heterogeneous panel data models and structural break models as special cases. The least squares method proposed by Bai (1997a, 2010) is applied to estimate the common change points, and the consistency …
On Testing For Sphericity With Non-Normality In A Fixed Effects Panel Data Model, Badi H. Baltagi, Chihwa Kao, Bin Peng
On Testing For Sphericity With Non-Normality In A Fixed Effects Panel Data Model, Badi H. Baltagi, Chihwa Kao, Bin Peng
Center for Policy Research
Building upon the work of Chen et al. (2010), this paper proposes a test for sphericity of the variance-covariance matrix in a fixed effects panel data regression model without the normality assumption on the disturbances.
A Lagrange Multiplier Test For Cross-Sectional Dependence In A Fixed Effects Panel Data Model, Badi Baltagi, Qu Feng, Chihwa Kao
A Lagrange Multiplier Test For Cross-Sectional Dependence In A Fixed Effects Panel Data Model, Badi Baltagi, Qu Feng, Chihwa Kao
Center for Policy Research
It is well known that the standard Breusch and Pagan (1980) LM test for cross-equation correlation in a SUR model is not appropriate for testing cross-sectional dependence in panel data models when the number of cross-sectional units (n) is large and the number of time periods (T) is small. In fact, a scaled version of this LM test was proposed by Pesaran (2004) and its finite sample bias was corrected by Pesaran, Ullah and Yamagata (2008). This was done in the context of a heterogeneous panel data model. This paper derives the asymptotic bias of this scaled version of the …