Small-Sample Properties of Nonlinear Least Squares and Maximum Likelihood Estimators in the Context of Autocorrelated Errors
- 1 March 1979
- journal article
- research article
- Published by JSTOR in Journal of the American Statistical Association
- Vol. 74 (365) , 41
- https://doi.org/10.2307/2286718
Abstract
Rao and Griliches (1969) compared several methods of parameter estimation in models having autocorrelated errors. They concluded that nonlinear least squares estimators were not superior to two-stage linear estimators. This study partially replicates the Rao and Griliches Monte Carlo simulation and, in addition, examines the maximum likelihood estimator as a possible competitor. The simulation results are not consistent with those of Rao and Griliches; the small-sample efficiency of nonlinear and maximum likelihood estimators appears to be consistently high and thus reverses some of Rao and Griliches's conclusions.Keywords
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