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Spatio-Temporal Instrumental Variables Regression with Missing Data: A Bayesian Approach

Author

Listed:
  • Marcus L. Nascimento

    (Universidade Federal do Rio de Janeiro)

  • Kelly C. M. Gonçalves

    (Universidade Federal do Rio de Janeiro)

  • Mario Jorge Mendonça

    (Instituto de Pesquisa Econômica Aplicada)

Abstract

This paper proposes an extension of the Bayesian instrumental variables regression which allows spatial and temporal correlation among observations. For that, we introduce a double separable covariance matrix, adopting a Conditional Autoregressive structure for the spatial component, and a first-order autoregressive process for the temporal component. We also introduce a Bayesian multiple imputation to handle missing data considering uncertainty. The inference procedure is described joint with a step by step Monte Carlo Markov Chain algorithm for parameters estimation. We illustrate our methodology through a simulation study and a real application that investigates how broadband affects the Gross Domestic Product of municipalities in the state of Mato Grosso do Sul from 2010 to 2017.

Suggested Citation

  • Marcus L. Nascimento & Kelly C. M. Gonçalves & Mario Jorge Mendonça, 2023. "Spatio-Temporal Instrumental Variables Regression with Missing Data: A Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 29-47, June.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:1:d:10.1007_s10614-022-10269-z
    DOI: 10.1007/s10614-022-10269-z
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    References listed on IDEAS

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