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Multivariate effect priors in bivariate semiparametric recursive Gaussian models

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  • Thaden, Hauke
  • Klein, Nadja
  • Kneib, Thomas

Abstract

Modeling complex relationships and interactions between variables is an ongoing statistical challenge. In particular, the joint modeling of multiple response variables is of great interest in methodological and applied research. Within this context the incorporation of semiparametric predictors into Bayesian recursive simultaneous equation models is considered. Extending the existing framework by imposing effect priors that account for potential correlation of the effects across equations allows for borrowing strength across equations as with multivariate conditional autoregressive priors used for the analysis of multivariate spatial data. A Gibbs sampler is implemented for the estimation and evaluated in an elaborate simulation study where the intra-equation correlation allows for more efficient posterior estimation. The applicability of the novel modeling approach is illustrated with real data examples on malnutrition in Asia and Africa as well as the analysis of plant and species richness with respect to environmental diversity.

Suggested Citation

  • Thaden, Hauke & Klein, Nadja & Kneib, Thomas, 2019. "Multivariate effect priors in bivariate semiparametric recursive Gaussian models," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 51-66.
    • Handle: RePEc:eee:csdana:v:137:y:2019:i:c:p:51-66
    DOI: 10.1016/j.csda.2018.12.004
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    References listed on IDEAS

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