SystemOfEquationsBiblio.pdf
© 2007, Timothy G. Gregoire
Yale University
Last revised: January 2007
SYSTEM OF EQUATIONS BIBLIOGRAPHY
1972-Present (18 entries)
1. Mikhail, W.M. (1972a). Simulating the small-sample properties of econometric
estimators. Journal of the American Statistical Association 67(339): 620-624.
2. Mikhail, W.M. (1972b). The bias of the two-stage least squares estimator. Journal of the
American Statistical Association 67(339): 625-627.
3. Kelly, J.S. (1975). Linear cross-equation constraints and the identification problem.
Econometrica 43(1): 125-140.
4. Mikhail, W.M. (1975). A comparative Monte Carlo study of the properties of
econometric estimators. Journal of the American Statistical Association 70(349): 94104.
5. Amemiya, T. (1977). The maximum likelihood and the nonlinear three-stage least squares
estimator in the general nonlinear simultaneous equation model. Econometrica 45(4):
955-968.
6. Gallant, A.R. (1977). Three-stage least-squares estimation for a system of simultaneous,
nonlinear, implicit equations. Journal of Econometrics 5: 71-88.
7. Gallant, A.R. and Jorgenson, D.W. (1979). Statistical inference for a system of
simultaneous, non-linear, implicit equations in the context of instrumental variable
estimation. Journal of Econometrics 11: 275-302.
8. Swamy, P.A.V.B. (1980). A comparison of estimators for undersized samples. Journal of
Econometrics 14: 161-181.
9. Wilson, B.K. (1985). Simultaneity and its impact on ecological regression applications.
Biometrics 41: 435-445.
10. Borders, B.E. (1989). Systems of equations in forest stand modeling. Forest Science
35(2): 548-556.
11. Spanos, A. (1990). The simultaneous-equations model revisited: statistical adequacy and
identification. Journal of Econometrics 44: 87-105.
12. Schmidt, P. (1990). Three-stage least squares with different instruments for different
equations. Journal of Econometrics 43: 389-394.
13. Hasenauer, H., Monserud, R.A. and Gregoire, T.G. (1996). Cross-correlations among
single tree growth models. In: Mowrer, H.T., R.L. Czaplewski, and R.H. Hamre (eds.).
Proceedings of 2nd International Symposium on Spatial Accuracy Assessment in
SystenOfEquationsBiblio.pdf
© 2004, Timothy G. Gregoire, Yale University
Natural Resources and Environmental Sciences. Fort Collins, Colorado. U.S.
Department of Agriculture, Forest Service, General Technical Report RM-GTR-277:
pp 667-675. (S1208, I 100).
14. Lynch, T.B. and Clutter, M.L. (1998). A system of equations for prediction of plywood
veneer total yield and yield by grade for loblolly pine plywood bolts. Forest Products
Journal 48(5): 80-88.
15. Lin, X., Ryan, L., Sammel, M., Zhang, D., Padungtod, C. and Xu, X. (2000). A scaled
linear mixed model for multiple outcomes. Biometrics 56(2): 593-601.
16. Geys, H., Regan, M.M., Catalano, P.J. and Molenberghs, G. (2001). Two latent variable
risk assessment approaches for mixed continuous and discrete outcomes from
developmental toxic data. Journal of Agricultural, Biological, and Environmental
tatistics 6(3): 340-355.
17. Rose, C.E., Jr. and Lynch, T.B. (2001). Estimating parameters for tree basal area growth
with a system of equations and seemingly unrelated regressions. Forest Ecology and
Management 148: 51-61. (Also in SUR folder)
18. Green, E.J., Strawderman, W.E., Amateis, R.L. and Reams, G.A. (2005). Improved
estimation for multiple means with heterogeneous variances. Forest Science 51(1): 1-6.
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