IDEAS home Printed from https://ideas.repec.org/a/gam/jecnmx/v8y2020i2p23-d365956.html
   My bibliography  Save this article

Bayesian Model Averaging with the Integrated Nested Laplace Approximation

Author

Listed:
  • Virgilio Gómez-Rubio

    (Department of Mathematics, School of Industrial Engineering, Universidad de Castilla-La Mancha, E-02071 Albacete, Spain
    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.)

  • Roger S. Bivand

    (Department of Economics, Norwegian School of Economics, 5045 Bergen, Norway
    These authors contributed equally to this work.)

  • Håvard Rue

    (CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Virgilio Gómez-Rubio & Roger S. Bivand & Håvard Rue, 2020. "Bayesian Model Averaging with the Integrated Nested Laplace Approximation," Econometrics, MDPI, vol. 8(2), pages 1-15, June.
    • Handle: RePEc:gam:jecnmx:v:8:y:2020:i:2:p:23-:d:365956
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2225-1146/8/2/23/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2225-1146/8/2/23/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    2. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    3. Solmaria Halleck Vega & J. Paul Elhorst, 2015. "The Slx Model," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 339-363, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. John M. Humphreys & Robert B. Srygley & David H. Branson, 2022. "Geographic Variation in Migratory Grasshopper Recruitment under Projected Climate Change," Geographies, MDPI, vol. 2(1), pages 1-19, January.
    2. Zongyuan Xia & Bo Tang & Long Qin & Huiguo Zhang & Xijian Hu, 2023. "Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
    3. Virgilio Gómez-Rubio & Roger S. Bivand & Håvard Rue, 2021. "Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation," Mathematics, MDPI, vol. 9(17), pages 1-23, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas Suesse, 2018. "Estimation of spatial autoregressive models with measurement error for large data sets," Computational Statistics, Springer, vol. 33(4), pages 1627-1648, December.
    2. Kuschnig, Nikolas, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Paper Series 318, WU Vienna University of Economics and Business.
    3. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    4. Wu, Wenchao, 2017. "Agglomeration Economy and Input-output Linkage: Evidence from the Entry of the Agro-food Industry in China," Japanese Journal of Agricultural Economics (formerly Japanese Journal of Rural Economics), Agricultural Economics Society of Japan (AESJ), vol. 19.
    5. John M. Humphreys, 2022. "Amplification in Time and Dilution in Space: Partitioning Spatiotemporal Processes to Assess the Role of Avian-Host Phylodiversity in Shaping Eastern Equine Encephalitis Virus Distribution," Geographies, MDPI, vol. 2(3), pages 1-16, July.
    6. Humphreys, John M. & Srygley, Robert B. & Lawton, Douglas & Hudson, Amy R. & Branson, David H., 2022. "Grasshoppers exhibit asynchrony and spatial non-stationarity in response to the El Niño/Southern and Pacific Decadal Oscillations," Ecological Modelling, Elsevier, vol. 471(C).
    7. Paul-Christian Bürkner & Jonah Gabry & Aki Vehtari, 2021. "Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models," Computational Statistics, Springer, vol. 36(2), pages 1243-1261, June.
    8. Yang, Anni & Liu, Chenhui & Yang, Di & Lu, Chaoru, 2023. "Electric vehicle adoption in a mature market: A case study of Norway," Journal of Transport Geography, Elsevier, vol. 106(C).
    9. Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.
    10. Álvaro Briz‐Redón & Jorge Mateu & Francisco Montes, 2022. "Identifying crime generators and spatially overlapping high‐risk areas through a nonlinear model: A comparison between three cities of the Valencian region (Spain)," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 97-120, February.
    11. Brian Witrick & Corey A. Kalbaugh & Lu Shi & Rachel Mayo & Brian Hendricks, 2021. "Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina," IJERPH, MDPI, vol. 19(1), pages 1-11, December.
    12. Nicholas J Tierney & Antonietta Mira & H Jost Reinhold & Giuseppe Arbia & Samuel Clifford & Angelo Auricchio & Tiziano Moccetti & Stefano Peluso & Kerrie L Mengersen, 2019. "Evaluating health facility access using Bayesian spatial models and location analysis methods," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-18, August.
    13. Rüttenauer, Tobias & Best, Henning, 2021. "Consistent Inequality across Germany? Exploring Spatial Heterogeneity in the Unequal Distribution of Air Pollution," SocArXiv 5tavs, Center for Open Science.
    14. Alexandra Fedorets & Franziska Lottmann & Michael Stops, 2019. "Job matching in connected regional and occupational labour markets," Regional Studies, Taylor & Francis Journals, vol. 53(8), pages 1085-1098, August.
    15. Nikoline N. Knudsen & Jörg Schullehner & Birgitte Hansen & Lisbeth F. Jørgensen & Søren M. Kristiansen & Denitza D. Voutchkova & Thomas A. Gerds & Per K. Andersen & Kristine Bihrmann & Morten Grønbæk , 2017. "Lithium in Drinking Water and Incidence of Suicide: A Nationwide Individual-Level Cohort Study with 22 Years of Follow-Up," IJERPH, MDPI, vol. 14(6), pages 1-13, June.
    16. Shuai Shi & Kathy Pain, 2020. "Investigating China’s Mid-Yangtze River economic growth region using a spatial network growth model," Urban Studies, Urban Studies Journal Limited, vol. 57(14), pages 2973-2993, November.
    17. Leonardo Padilla & Bernado Lagos‐Álvarez & Jorge Mateu & Emilio Porcu, 2020. "Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    18. Scott, Ryan P. & Scott, Tyler A., 2019. "Investing in collaboration for safety: Assessing grants to states for oil and gas distribution pipeline safety program enhancement," Energy Policy, Elsevier, vol. 124(C), pages 332-345.
    19. Guohuan Su & Adam Mertel & Sébastien Brosse & Justin M. Calabrese, 2023. "Species invasiveness and community invasibility of North American freshwater fish fauna revealed via trait-based analysis," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    20. Cho, Daegon & Hwang, Youngdeok & Park, Jongwon, 2018. "More buzz, more vibes: Impact of social media on concert distribution," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 103-113.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecnmx:v:8:y:2020:i:2:p:23-:d:365956. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.