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The Iterated Minimum Distance Estimator and the Quasi-Maximum Likelihood Estimator
P. C. B. Phillips
Abstract
A multiple equation nonlinear regression model with serially independent disturbances is considered. The estimation of the parameters in this model by maximum likelihood and minimum distance methods is discussed and our main subject is the relationship between these procedures. We establish that if the number of observations in a sample is sufficiently large, the iterated minimum distance procedure converges almost surely and the limit of this sequence of iterations is the quasi-maximum likelihood estimator.
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