Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation

Virgilio Gómez-Rubio, Roger Bivand and Håvard Rue
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Virgilio Gómez-Rubio: Department of Mathematics, School of Industrial Engineering, University of Castilla-La Mancha, 02071 Albacete, Spain
Håvard Rue: The Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia

Mathematics, 2021, vol. 9, issue 17, 1-23

Abstract: The integrated nested Laplace approximation (INLA) provides a fast and effective method for marginal inference in Bayesian hierarchical models. This methodology has been implemented in the R-INLA package which permits INLA to be used from within R statistical software. Although INLA is implemented as a general methodology, its use in practice is limited to the models implemented in the R-INLA package. Spatial autoregressive models are widely used in spatial econometrics but have until now been lacking from the R-INLA package. In this paper, we describe the implementation and application of a new class of latent models in INLA made available through R-INLA . This new latent class implements a standard spatial lag model. The implementation of this latent model in R-INLA also means that all the other features of INLA can be used for model fitting, model selection and inference in spatial econometrics, as will be shown in this paper. Finally, we will illustrate the use of this new latent model and its applications with two data sets based on Gaussian and binary outcomes.

Keywords: Bayesian inference; INLA; R; spatial econometrics; spatial statistics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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