Full Information Estimation

  • Thomas B. Fomby
  • Stanley R. Johnson
  • R. Carter Hill


In the previous chapter, we discussed methods of estimation for systems of simultaneous equations that utilized information pertaining to a particular equation alone but not that related to other equations nor the fact that the structural disturbances of various equations are contemporaneously correlated. In this chapter, however, methods are introduced that utilize both intra- and inter-equation information. As we shall see, the utilization of this additional information leads to a gain in the efficiency of estimation. In addition, this chapter discusses the implementation and testing of linear hypotheses in simultaneous equations models that may arise from a priori economic reasoning. Apart from the information implicit in a simultaneous equations model itself, the utilization of linear hypotheses can lead to extra gains in efficiency.


Full Information Simultaneous Equation Full Information Maximum Likelihood Simultaneous Equation Model Asymptotic Covariance Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Amemiya, T. (1977). The maximum likelihood and nonlinear three-stage least squares estimator in the general nonlinear simultaneous equation model. Econometrica, 45, 955–968.CrossRefGoogle Scholar
  2. Belsley, D. (1979). On the computational competitiveness of full-information maximum likelihood and three-stage least squares in the estimation of nonlinear simultaneous equations systems. Journal of Econometrics, 9, 315–342.CrossRefGoogle Scholar
  3. Belsley, D. (1980). On the efficient computation of the nonlinear full information maximum likelihood estimator. Journal of Econometrics, 14, 203–226.CrossRefGoogle Scholar
  4. Brown, B. (1981). Sample size requirements in full information maximum likelihood estimation. International Economic Review, 22, 443–460.CrossRefGoogle Scholar
  5. Brundy, J. and Jorgensen, D. (1971). Efficient estimation of simultaneous equations by instrumental variables. Review of Economics and Statistics, 53, 207–224.CrossRefGoogle Scholar
  6. Byron, R. (1976). A reinterpretation of two and three stage least squares. International Economic Review, 17, 773–778.CrossRefGoogle Scholar
  7. Byron, R. (1977). Efficient estimation and inference in large econometric systems. Econometrica, 45, 1499–1516.CrossRefGoogle Scholar
  8. Dagenais, M. (1978). The computation of FIML estimates as iterative generalized least squares estimates in linear and nonlinear simultaneous equation models. Econometrica, 46, 1351–1363.CrossRefGoogle Scholar
  9. Dent, W. (1976). Information and computation in simultaneous equation estimation. Journal of Econometrics, 4, 89–95.CrossRefGoogle Scholar
  10. Dhrymes, P. and Erlat, H. (1974). Asymptotic properties of full information estimators in dynamic autoregressive simultaneous equations models. Journal of Econometrics, 2, 247–259.CrossRefGoogle Scholar
  11. Fair, R. and Parke, W. (1980). Full information estimates of a nonlinear macroeconometric model. Journal of Econometrics, 13, 269–292.CrossRefGoogle Scholar
  12. Gallant, A. (1977). Three-stage least squares estimation for a system of simultaneous, nonlinear, implicit equations. Journal of Econometrics, 5, 71–88.CrossRefGoogle Scholar
  13. Gallant, A. and Holly, A. (1980). Statistical inference in an implicit, nonlinear simultaneous equation model in the context of maximum likelihood estimation. Econometrica, 48, 697–720.CrossRefGoogle Scholar
  14. Gallant, A. and Jorgenson, D. (1979). Statistical inference for a system of simultaneous, nonlinear, implicit equations in the context of instrumental variable estimation. Journal of Econometrics, 11, 275–302.CrossRefGoogle Scholar
  15. Hausman, J. (1975). An instrumental variable approach to full information estimation for linear and certain nonlinear econometric models. Econometrica, 43, 727–738.CrossRefGoogle Scholar
  16. Hendry, D. (1976). The structure of simultaneous equations estimators. Journal of Econometrics, 4, 51–88.CrossRefGoogle Scholar
  17. Jennings, L. (1980). Simultaneous equations estimation: computational aspects. Journal of Econometrics, 12, 23–41.CrossRefGoogle Scholar
  18. Maasoumi, E. (1980). A ridge-like method for simultaneous estimation of simultaneous equations. Journal of Econometrics, 12, 161–176.CrossRefGoogle Scholar
  19. Maravall, A. (1976). A note on three stage least squares estimation. Journal of Econometrics, 4, 325–330.CrossRefGoogle Scholar
  20. Parke, W. (1982). An algorithm for FIML and 3SLS estimation for large nonlinear models. Econometrica, 50, 81–97.CrossRefGoogle Scholar
  21. Reinsei, G. (1979). FIML estimation of the dynamic simultaneous equation models with ARMA disturbances. Journal of Econometrics, 9, 263–281.CrossRefGoogle Scholar
  22. Rothenberg, T. (1973). Efficient Estimation with A Priori Information. New Haven, CT: Yale University Press.Google Scholar
  23. Rothenberg, T. and Leenders, C. (1964). Efficient estimation of simultaneous equation systems. Econometrica, 32, 406–425.Google Scholar
  24. Scharf, W. (1976). K-matrix class estimators and the full information maximum likelihood estimator as a special case. Journal of Econometrics, 4, 41–50.CrossRefGoogle Scholar
  25. Schmidt, P. (1976). Econometrics. New York: Marcel Dekker.Google Scholar
  26. Summers, R. (1965). A capital intensive approach to the small sample properties of various simultaneous equation estimators. Econometrica, 33, 1–41.CrossRefGoogle Scholar
  27. Wegge, L. (1978). Constrained indirect least squares. Econometrica, 46, 435–450.CrossRefGoogle Scholar
  28. Yeh, C. J. (1976). Prices, farm outputs, and income projections under alternative assumed demand and supply conditions. American Journal of Agricultural Economics, 58, 703–711.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1984

Authors and Affiliations

  • Thomas B. Fomby
    • 1
  • Stanley R. Johnson
    • 2
  • R. Carter Hill
    • 3
  1. 1.Department of EconomicsSouthern Methodist UniversityDallasUSA
  2. 2.The Center for Agricultural and Rural DevelopmentIowa State UniversityAmesUSA
  3. 3.Department of EconomicsLouisiana State UniversityBaton RougeUSA

Personalised recommendations