 A Comparison of FIML and Robust Estimates of a Nonlinear Macroeconomic ModelRay Fair () No 15, NBER Working Papers from National Bureau of Economic Research, Inc 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. Date: 1973-10 Published as Fair, Ray C. "On the Robust Estimation of Econometric Models." Annals of Economic and Social Measurement, Vol. 3, No. 4, (October 1974), pp. 667-677. Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:0015 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.