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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. [ABSTRACT FROM AUTHOR]

The software for spatial econometrics available in the R system for statistical computing is reviewed. The methods are illustrated in a historical perspective, highlighting the main lines of development and employing historically relevant datasets in the examples. Estimators and tests for spatial cross-sectional and panel models based either on maximum likelihood or on generalized moments methods are presented. The paper is concluded reviewing some current active lines of research in spatial econometric software methods. [ABSTRACT FROM AUTHOR]