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What are the advantages of MCMC based inference in latent variable models?

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  • Richard Paap

Abstract

Recent developments in Markov chain Monte Carlo [MCMC] methods have increased the popularity of Bayesian inference in many fields of research in economics, such as marketing research and financial econometrics. Gibbs sampling in combination with data augmentation allows inference in statistical/econometric models with many unobserved variables. The likelihood functions of these models may contain many integrals, which often makes a standard classical analysis difficult or even unfeasible. The advantage of the Bayesian approach using MCMC is that one only has to consider the likelihood function conditional on the unobserved variables. In many cases this implies that Bayesian parameter estimation is faster than classical maximum likelihood estimation. In this paper we illustrate the computational advantages of Bayesian estimation using MCMC in several popular latent variable models.

Suggested Citation

  • Richard Paap, 2002. "What are the advantages of MCMC based inference in latent variable models?," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(1), pages 2-22, February.
    • Handle: RePEc:bla:stanee:v:56:y:2002:i:1:p:2-22
    DOI: 10.1111/1467-9574.00060
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    References listed on IDEAS

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    1. Paap, Richard & van Nierop, Erjen & van Heerde, Harald J. & Wedel, Michel & Franses, Philip Hans & Alsem, Karel Jan, 2005. "Consideration sets, intentions and the inclusion of "don't know" in a two-stage model for voter choice," International Journal of Forecasting, Elsevier, vol. 21(1), pages 53-71.
    2. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653, November.
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    1. Yang, Yang & Longini Jr., Ira M. & Elizabeth Halloran, M., 2007. "A data-augmentation method for infectious disease incidence data from close contact groups," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6582-6595, August.
    2. Chonu, Gi Kunchana, 2013. "Comparison between Maximum Likelihood and Bayesian Estimation in Structural Equation Modelling and Effects of Informative Priors," Thesis Commons xef3g, Center for Open Science.
    3. Martin Burda & Roman Liesenfeld & Jean-Francois Richard, 2008. "Bayesian Analysis of a Probit Panel Data Model with Unobserved Individual Heterogeneity and Autocorrelated Errors," Working Papers tecipa-321, University of Toronto, Department of Economics.
    4. Vica Tendenan & Richard Gerlach & Chao Wang, 2020. "Tail risk forecasting using Bayesian realized EGARCH models," Papers 2008.05147, arXiv.org, revised Aug 2020.
    5. Lydia Simon & Jost Adler, 2022. "Worth the effort? Comparison of different MCMC algorithms for estimating the Pareto/NBD model," Journal of Business Economics, Springer, vol. 92(4), pages 707-733, May.
    6. Luca Grassetti, 2011. "A note on transformed likelihood approach in linear dynamic panel models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(2), pages 221-240, June.
    7. Taghreed Alghamdi & Khalid Elgazzar & Taysseer Sharaf, 2021. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling," Future Internet, MDPI, vol. 13(9), pages 1-18, August.

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