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A Comparison of FIML and Robust Estimates of a Nonlinear Macroeconomic Model

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  • Ray C. Fair

Abstract

The prediction accuracy of six estimators of econometric models are compared. Two of rthe estimators are ordinary least squares (OLS) and full-information maximum likelihood. (FML). The other four estimators are robust estimators in the sense that they give less weight to large residuals. One of the four estimators is approximately equivalent to the least-absolute-residual (LAR) estimator, one is a combination of OLS for small residuals and LAR for large residuals, one is an estimator proposed by John W. Tukey, and one is a combination of FIML and LAR. All of the estimators account for the first-order serial correlation of the error terms. The main conclusion is that robust estimators appear quite promising for the estimation of econometric models. Of the robust estimators considered in the paper, the one based on minimizing the sum of the absolute values of the residuals performed the best. The FIML estimator and the combination of the FIML and LAR estimators also appear promising.

Suggested Citation

  • Ray C. Fair, 1973. "A Comparison of FIML and Robust Estimates of a Nonlinear Macroeconomic Model," NBER Working Papers 0015, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:0015
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    References listed on IDEAS

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    1. Chow, Gregory C, 1973. "On the Computation of Full-Information Maximum Likelihood Estimates for Nonlinear Equation Systems," The Review of Economics and Statistics, MIT Press, vol. 55(1), pages 104-109, February.
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    Cited by:

    1. David A. Belsley & Kent D. Wall, 1976. "Estimation of Econometric Model Using Nonlinear Full Information Maximum Likelihood: Preliminary Computer Results," NBER Working Papers 0142, National Bureau of Economic Research, Inc.

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