Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation

被引:13
|
作者
Gomez-Rubio, Virgilio [1 ]
Bivand, Roger S. [2 ]
Rue, Havard [3 ]
机构
[1] Univ Castilla La Mancha, Sch Ind Engn, Dept Math, Albacete 02071, Spain
[2] Norwegian Sch Econ, Dept Econ, N-5045 Bergen, Norway
[3] King Abdullah Univ Sci & Technol, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 239556900, Saudi Arabia
关键词
Bayesian inference; INLA; R; spatial econometrics; spatial statistics; NETWORK ECONOMETRICS; BAYESIAN TECHNIQUES; INFERENCE;
D O I
10.3390/math9172044
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
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.
引用
收藏
页数:23
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