Developing Ridge Parameters for SUR Model

References

• Alkhamisi , M. A. , Shukur , G. ( 2007 ). A Monte Carlo study of recent ridge parameters . Commun. Statist. Simul. Compu. 36 : 535 – 547 .
   Web of Science ®Google Scholar
• Alkhamisi , M. A. , Khalaf , G. , Shukur , G. ( 2006 ). Some modifications for choosing ridge parameter . Commun. Statist. Thoery Methods 35 : 1 – 16 .
   Web of Science ®Google Scholar
• Brown , P. J. , Payne , C. ( 1975 ). Election night forecasting (with discussion) . J. Roy. Statist. Soc. Ser. A 138 : 463 – 498 .
   Google Scholar
• Brown , P. J. , Zidek , J. V. ( 1980 ). Adaptive multivariate ridge regression . . 8(1):64–74.
   Google Scholar
• Chib , S. , Greenberg , E. ( 1995 ). Hierarchical analysis of SUR model with extensions to correlated serial errors and time varying parameter models . J. Econometrics 68 : 339 – 360 .
   Web of Science ®Google Scholar
• Edgerton , D. L. , Assarsson , B. , Hummelmose , A. , Laurila , I. P. , Ricertsen , K. , Vlae , P. H. ( 1996 ). The Econometrics of Demand Systems . Dordrecht : Kluwer Academic Press .
   Google Scholar
• Fiebig , G. D. , Kim , H. J. ( 2000 ). Estimation and inference in SUR models when the number of equations is large . Econometric Rev. 19 ( 1 ): 105 – 130 .
   Google Scholar
• Firinguetti , L. (1997). Ridge regression in the context of a system of seemingly unrelated regression equations. J. Statist. Comput. Simul. 56:145–62.
   Web of Science ®Google Scholar
• Frisch , R. A. ( 1934 ). Statistical Confluence Analysis by Means of Complete Regression Systems . Publication 5 . Oslo : University Institute of Economics 1934 .
   Google Scholar
• Haitovsky , Y. ( 1987 ). On multivariate ridge regression . Biometrika 74 : 563 – 570 .
   Web of Science ®Google Scholar
• Hoerl , A. E. , Kennard , R. W. ( 1970a ). Ridge regression: Biased estimation for non-orthogonal problems . Tech. 12 : 55 – 67 .
   Web of Science ®Google Scholar
• Hoerl , A. E. , Kennard , R. W. ( 1970b ). Ridge regression: application to non-orthogonal problems . Tech. 12 : 69 – 82 .
   Web of Science ®Google Scholar
• Hoerl , A. E. , Kennard , R. W. , Baldwin , K.F. ( 1975 ). Ridge regression—some simulations . Commun. Statist. B 4 : 105 – 123 .
   Google Scholar
• Kibria , B. M. G. ( 2003 ). Performance of some new ridge regression estimators . Commun. Statist. B 32 : 419 – 435 .
   Google Scholar
• Koop , G. ( 2003 ). Bayesian Econometrics . New York : John Wiley & Sons .
   Google Scholar
• Rao , C. R. ( 1973 ). Linear Statistical Inference and Its Applications . New York : Wiley .
   Google Scholar
• Rao , C. R. ( 1975 ). Simultaneous estimation of parameters in different linear models and applications to biometric problems . Biometrics 3 ( 2 ): 545 – 554 .
   Web of Science ®Google Scholar
• Saleh , A. K. Md. E. , Kibiria , B. M. G. ( 1993 ). Performance of some new preliminary test ridge regression estimators and their properties . Commun. Statist. A 22 : 2747 – 2764 .
   Google Scholar
• Shukur , G. ( 2002 ). Dynamic specification and misspecification in systems of demand equations: A testing strategy for model selection . Applied Economics 34 : 709 – 725 .
   Web of Science ®Google Scholar
• Srivastava , V. , Giles , D. ( 1987 ). Seemingly Unrelated Regression Equations Models . New York : Marcel Dekker .
   Google Scholar
• Vinod , H. D. ( 1978 ). A survey of ridge regression and related techniques for improvements over ordinary least squares . Rev. Econ. Stat. 60 : 121 – 131 .
   Web of Science ®Google Scholar
• Zellner , A. ( 1962 ). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias . J. Am. Statist. Assoc. 57 : 348 – 368 .
   Web of Science ®Google Scholar