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

29 Pages Posted: 6 Dec 2006 Last revised: 19 Jul 2010

See all articles by Ray C. Fair

Ray C. Fair

Yale University - Cowles Foundation; Yale School of Management - International Center for Finance

Date Written: October 1973

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

Fair, Ray C., A Comparison of Fiml and Robust Estimates of a Nonlinear Macroeconomic Model (October 1973). NBER Working Paper No. w0015. Available at SSRN: https://ssrn.com/abstract=259328

Ray C. Fair (Contact Author)

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
United States
203-432-3715 (Phone)
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HOME PAGE: http://fairmodel.econ.yale.edu

Yale School of Management - International Center for Finance ( email )

Box 208200
New Haven, CT 06520
United States
203-432-3715 (Phone)
203-432-6167 (Fax)

HOME PAGE: http://fairmodel.econ.yale.edu

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