Abstract
In this study, we present a new method, called a tensor method, for the computation of unconstrained Full-Information Maximum Likelihood (FIML) estimates. The new techniqus is based upon a fourth order approximation to the log-likelihood function, rather than the second order approximation used in standard methods. The higher order terms are low rank third and fourth order tensors that are computed, at very little storage or computation cost, using information from previous iterations. We form and solve the tensor model, then present test results showing that the tensor method is far more efficient than the standard Newton's method for a wide range of unconstrained FIML estimation problems.
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This paper is based upon part of my doctoral dissertation at George Washington University. I would like to thank my committee members, Professors Robert Phillips and Frederick Joutz of George Washington University and John R. Norsworthy of Renssalaer Polytechnic Institute for their support and suggestions. Any errors remaining are my own.
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Greenblatt, S.A. Tensor methods for full-information maximum likelihood estimation: Unconstrained estimation. Comput Econ 7, 89–108 (1994). https://doi.org/10.1007/BF01299569
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DOI: https://doi.org/10.1007/BF01299569