Sparse seemingly unrelated regression modelling: Applications in finance and econometrics
AbstractA 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 54 (2010)
Issue (Month): 11 (November)
Contact details of provider:
Web page: http://www.elsevier.com/locate/csda
Dynamic linear model Gaussian graphical model Hyper-inverse Wishart distribution Marginal likelihood Markov chain Monte Carlo Variable selection;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Min, C.K. & Zellner, A., 1992.
""Bayesian and Non-Bayesian Methods for Combining Models and Forecasts with Applications to Forecasting International Growth Rates","
90-92-23, California Irvine - School of Social Sciences.
- 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.
- Smith, M. & Kohn, R., 1998.
"Nonparametric Seemingly Unrelated Regression,"
Monash Econometrics and Business Statistics Working Papers
7/98, Monash University, Department of Econometrics and Business Statistics.
- 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.
- 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.
- 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.
- 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.
- Hao Wang & Mike West, 2009. "Bayesian analysis of matrix normal graphical models," Biometrika, Biometrika Trust, vol. 96(4), pages 821-834.
- Carlos M. Carvalho & Hél�ne Massam & Mike West, 2007. "Simulation of hyper-inverse Wishart distributions in graphical models," Biometrika, Biometrika Trust, vol. 94(3), pages 647-659.
- 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.
- Griffiths, W.E., 2001. "Bayesian Inference in the Seemingly Unrelated Regressions Models," Department of Economics - Working Papers Series 793, The University of Melbourne.
- 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.
- Fama, Eugene F, 1981. "Stock Returns, Real Activity, Inflation, and Money," American Economic Review, American Economic Association, vol. 71(4), pages 545-65, September.
- 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.
- 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.
- C. M. Carvalho & J. G. Scott, 2009. "Objective Bayesian model selection in Gaussian graphical models," Biometrika, Biometrika Trust, vol. 96(3), pages 497-512.
- 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.
- 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 1591-603, September.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
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 references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link 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 profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.