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

Marcus L. Nascimento (), Kelly C. M. Gonçalves and Mario Jorge Mendonça
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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

Computational Economics, 2023, vol. 62, issue 1, No 2, 29-47

Abstract: 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.

Keywords: IV regression; Endogeneity; Bayesian inference; Gibbs sampling; Metropolis–Hastings algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10269-z

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