Bayesian Model Averaging with the 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, Universidad de Castilla-La Mancha, E-02071 Albacete, Spain
Håvard Rue: CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia

Econometrics, 2020, vol. 8, issue 2, 1-15

Abstract: 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.

Keywords: Bayesian model averaging; INLA; spatial econometrics (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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