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Bayesian Model Averaging with the Integrated Nested Laplace Approximation

by Virgilio Gómez-Rubio 1,*,†,‡, Roger S. Bivand 2,‡ and Håvard Rue 3,‡
1
Department of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, E-02071 Albacete, Spain
2
Department of Economics, Norwegian School of Economics, 5045 Bergen, Norway
3
CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
*
Author to whom correspondence should be addressed.
Current address: Department of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, Avda. España s/n, 02071 Albacete, Spain.
These authors contributed equally to this work.
Econometrics 2020, 8(2), 23; https://doi.org/10.3390/econometrics8020023
Received: 25 October 2019 / Revised: 19 February 2020 / Accepted: 20 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent Gaussian Markov random fields (GMRF). The representation as a GMRF allows the associated software R-INLA to estimate the posterior marginals in a fraction of the time as typical Markov chain Monte Carlo algorithms. INLA can be extended by means of Bayesian model averaging (BMA) to increase the number of models that it can fit to conditional latent GMRF. In this paper, we review the use of BMA with INLA and propose a new example on spatial econometrics models. View Full-Text
Keywords: Bayesian model averaging; INLA; spatial econometrics Bayesian model averaging; INLA; spatial econometrics
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Gómez-Rubio, V.; Bivand, R.S.; Rue, H. Bayesian Model Averaging with the Integrated Nested Laplace Approximation. Econometrics 2020, 8, 23.

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