Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating Spatial Econometrics Models with Integrated Nested Laplace
  Approximation   Integrated Nested Laplace Approximation provides a fast and effective method
for marginal inference on 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 missing 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, which is widely used and that can be used to build more complex
models in spatial econometrics.
  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
datasets based on Gaussian and binary outcomes.

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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: math
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
1
The categories of this paper are: cs
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con