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Abstract
Existing studies on spatial panel data models typically assume a normal distribution for the random error components. This assumption may not be appropriate in many applications. Here we consider a more flexible and powerful approach that generalizes the traditional model. We propose a skew-normal generalized spatial panel data model that adopts a multivariate skew normal distribution for the random error components. For parameter estimation, a Bayesian inference algorithm is developed. A simulation study and the analysis of a real data set of cigarette demand are conducted to compare the proposed skew normal spatial model with the traditional (normal) spatial model.
Acknowledgments
The authors thank the editor and referees for their constructive comments which significant improved this paper. The first and second authors are also grateful to the Shahid Chamran university of Ahvaz for their financial support. We would like to thank Dr. Sharon Lee of the Department of Mathematics, University of Queensland for her valuable comments which enhanced the preparation of this paper.