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Sparse seemingly unrelated regression modelling: Applications in finance and econometrics

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  • Wang, Hao
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    Abstract

    A sparse seemingly unrelated regression (SSUR) model is proposed to generate substantively relevant structures in the high-dimensional distributions of seemingly unrelated regression (SUR) model parameters. The SSUR framework includes prior specifications, posterior computations using Markov chain Monte Carlo methods, evaluations of model uncertainty, and model structure searches. Extensions of the SSUR model to dynamic models embed general structure constraints and model uncertainty in dynamic models. The models represent specific varieties of models recently developed in the growing high-dimensional sparse modelling literature. Two simulated examples illustrate the model and highlight questions regarding model uncertainty, searching, and comparison. The model is then applied to two real-world examples in macroeconomics and finance, according to which its identified structures have practical significance.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 54 (2010)
    Issue (Month): 11 (November)
    Pages: 2866-2877

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    Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2866-2877

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Dynamic linear model Gaussian graphical model Hyper-inverse Wishart distribution Marginal likelihood Markov chain Monte Carlo Variable selection;

    References

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    Cited by:
    1. Jouchi Nakajima & Mike West, 2013. "Bayesian Analysis of Latent Threshold Dynamic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 151-164, April.

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