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Sparse seemingly unrelated regression modelling: Applications in finance and econometrics

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  • Wang, Hao

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.

Suggested Citation

  • Wang, Hao, 2010. "Sparse seemingly unrelated regression modelling: Applications in finance and econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2866-2877, November.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:11:p:2866-2877
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:ecosta:v:3:y:2017:i:c:p:3-22 is not listed on IDEAS
    2. Monica Billio & Roberto Casarin & Luca Rossini, 2016. "Bayesian Nonparametric Sparse Seemingly Unrelated Regression Model (SUR)," Papers 1608.02740, arXiv.org, revised Jul 2017.
    3. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2016. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Annals of Economics and Statistics, GENES, pages 333-361.
    4. repec:gam:jecnmx:v:4:y:2016:i:1:p:13:d:65308 is not listed on IDEAS
    5. Urbi Garay & Enrique ter Horst & German Molina & Abel Rodriguez, 2016. "Bayesian Nonparametric Measurement of Factor Betas and Clustering with Application to Hedge Fund Returns," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-23, March.
    6. Zhou, Xiaocong & Nakajima, Jouchi & West, Mike, 2014. "Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 963-980.
    7. 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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