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Feasible Generalized Least Squares Estimation

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

Abstract

There are instances in econometric modeling when an investigator is willing to specify the structure of the error variance-covariance matrix, Ω, of a generalized least squares model up to a few unknown parameters, say θl θ2,..., θp. This would occur, for example, when correlation in the errors of a time series regression model is suspected or when cross-sections of data are expected to satisfy a regression relationship with varying precisions. These parametrizations of Ω, which previously have been discussed in general form, will be discussed in detail in the following chapters. For the present, we will continue to discuss Ω in general terms, not limiting our discussion in any way except that the parametrization of Ω is assumed to be parasimonious enough to allow estimation.

Keywords

Maximum Likelihood Estimator American Statistical Association Investment Equation Error Covariance Matrix Unrelated Regression 
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.

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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

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