Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247571 
Year of Publication: 
2020
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 8 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-15
Publisher: 
MDPI, Basel
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.
Subjects: 
Bayesian model averaging
INLA
spatial econometrics
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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