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Multivariate spatial regression models

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  • Gamerman, Dani
  • Moreira, Ajax R. B.

Abstract

This paper describes the inference procedures required to perform Bayesian inference to some multivariate econometric models. These models have a spatial component built into commonly used multivariate models. In particular, the common component models are addressed and extended to accommodate for spatial dependence. Inference procedures are based on a variety of simulation-based schemes designed to obtain samples from the posterior distribution of model parameters. They are also used to provide a basis to forecast new observations.

Suggested Citation

  • Gamerman, Dani & Moreira, Ajax R. B., 2004. "Multivariate spatial regression models," Journal of Multivariate Analysis, Elsevier, vol. 91(2), pages 262-281, November.
    • Handle: RePEc:eee:jmvana:v:91:y:2004:i:2:p:262-281
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    References listed on IDEAS

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    Cited by:

    1. Mkondiwa, Maxwell Gibson, 2015. "Whither Broad or Spatially Specific Fertilizer Recommendations?," Master's Theses and Plan B Papers 237344, University of Minnesota, Department of Applied Economics.
    2. Alexandra Schmidt & Ajax Moreira & Steven Helfand & Thais Fonseca, 2009. "Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 31(2), pages 101-112, April.
    3. Nascimento, Marcus Gerardus Lavagnole & Abanto-Valle, Carlos Antonio & Mendonça, Mario Jorge, 2019. "Multivariate Spatial IV Regression," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(2), January.
    4. Mário Jorge Mendonça & Paulo R. A. Loureiro & Antônio Nascimento & Roberto Ellery, 2021. "Assessment of the effect of broadband expansion on the economy reviewed," Review of Development Economics, Wiley Blackwell, vol. 25(4), pages 2414-2432, November.
    5. Lu Zhang & Sudipto Banerjee & Andrew O. Finley, 2021. "High‐dimensional multivariate geostatistics: A Bayesian matrix‐normal approach," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    6. Takahiro Yoshida & Morito Tsutsumi, 2018. "On the effects of spatial relationships in spatial compositional multivariate models," Letters in Spatial and Resource Sciences, Springer, vol. 11(1), pages 57-70, March.
    7. Nascimento, Marcus Gerardus Lavagnole & Abanto-Valle, Carlos Antonio & Mendonça, Mario Jorge, 2018. "Multivariate Spatial IV Regression," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 38(2).
    8. Lu Zhang & Sudipto Banerjee, 2022. "Spatial factor modeling: A Bayesian matrix‐normal approach for misaligned data," Biometrics, The International Biometric Society, vol. 78(2), pages 560-573, June.
    9. Reinaldo B. Arellano-Valle & Javier E. Contreras-Reyes & Freddy O. López Quintero & Abel Valdebenito, 2019. "A skew-normal dynamic linear model and Bayesian forecasting," Computational Statistics, Springer, vol. 34(3), pages 1055-1085, September.

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