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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.

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  • 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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    1. Smith, Michael & Kohn, Robert, 2000. "Nonparametric seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 98(2), pages 257-281, October.
    2. Kontoghiorghes, E. J. & Clarke, M. R. B., 1995. "An alternative approach for the numerical solution of seemingly unrelated regression equations models," Computational Statistics & Data Analysis, Elsevier, vol. 19(4), pages 369-377, April.
    3. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    4. C. M. Carvalho & J. G. Scott, 2009. "Objective Bayesian model selection in Gaussian graphical models," Biometrika, Biometrika Trust, vol. 96(3), pages 497-512.
    5. Griffiths, W.E., 2001. "Bayesian Inference in the Seemingly Unrelated Regressions Models," Department of Economics - Working Papers Series 793, The University of Melbourne.
    6. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
    7. Hans, Chris & Dobra, Adrian & West, Mike, 2007. "Shotgun Stochastic Search for," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 507-516, June.
    8. Lee, Bong-Soo, 1992. "Causal Relations among Stock Returns, Interest Rates, Real Activity, and Inflation," Journal of Finance, American Finance Association, vol. 47(4), pages 1591-1603, September.
    9. Fama, Eugene F, 1981. "Stock Returns, Real Activity, Inflation, and Money," American Economic Review, American Economic Association, vol. 71(4), pages 545-565, September.
    10. George, Edward I. & Sun, Dongchu & Ni, Shawn, 2008. "Bayesian stochastic search for VAR model restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 553-580, January.
    11. Foschi, Paolo & Kontoghiorghes, Erricos J., 2002. "Seemingly unrelated regression model with unequal size observations: computational aspects," Computational Statistics & Data Analysis, Elsevier, vol. 41(1), pages 211-229, November.
    12. P. J. Brown & M. Vannucci & T. Fearn, 1998. "Multivariate Bayesian variable selection and prediction," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 627-641.
    13. Carlos M. Carvalho & Hélène Massam & Mike West, 2007. "Simulation of hyper-inverse Wishart distributions in graphical models," Biometrika, Biometrika Trust, vol. 94(3), pages 647-659.
    14. Geske, Robert & Roll, Richard, 1983. "The Fiscal and Monetary Linkage between Stock Returns and Inflation," Journal of Finance, American Finance Association, vol. 38(1), pages 1-33, March.
    15. Hao Wang & Mike West, 2009. "Bayesian analysis of matrix normal graphical models," Biometrika, Biometrika Trust, vol. 96(4), pages 821-834.
    16. Michael J. Daniels, 2002. "Bayesian analysis of covariance matrices and dynamic models for longitudinal data," Biometrika, Biometrika Trust, vol. 89(3), pages 553-566, August.
    17. Foschi, Paolo & Belsley, David A. & Kontoghiorghes, Erricos J., 2003. "A comparative study of algorithms for solving seemingly unrelated regressions models," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 3-35, October.
    18. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
    19. Smith M. & Kohn R., 2002. "Parsimonious Covariance Matrix Estimation for Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1141-1153, December.
    20. Zhen Chen & David B. Dunson, 2003. "Random Effects Selection in Linear Mixed Models," Biometrics, The International Biometric Society, vol. 59(4), pages 762-769, December.
    21. Dobra, Adrian & Hans, Chris & Jones, Beatrix & Nevins, J.R.Joseph R. & Yao, Guang & West, Mike, 2004. "Sparse graphical models for exploring gene expression data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 196-212, July.
    Full references (including those not matched with items on IDEAS)

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    9. Peremans, Kris & Van Aelst, Stefan, 2018. "Robust inference for seemingly unrelated regression models," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 212-224.
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