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Skew-normal Bayesian spatial heterogeneity panel data models

Author

Listed:
  • Mohadeseh Alsadat Farzammehr
  • Mohammad Reza Zadkarami
  • Geoffrey J. McLachlan
  • Sharon X. Lee

Abstract

This paper proposes a new regression model for the analysis of spatial panel data in the case of spatial heterogeneity and non-normality. In empirical economic research, the normality of error components is a routine assumption for the models with continuous responses. However, such an assumption may not be appropriate in many applications. This work relaxes the normality assumption by using a multivariate skew-normal distribution, which includes the normal distribution as a special case. The methodology is illustrated through a simulation study and application to insurance and gasoline demand data sets. In these analyses, a simple Bayesian framework that implements a Markov chain Monte Carlo algorithm is derived for parameter estimation and inference.

Suggested Citation

  • Mohadeseh Alsadat Farzammehr & Mohammad Reza Zadkarami & Geoffrey J. McLachlan & Sharon X. Lee, 2020. "Skew-normal Bayesian spatial heterogeneity panel data models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(5), pages 804-826, April.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:5:p:804-826
    DOI: 10.1080/02664763.2019.1657812
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