Tensor Methods of Full-Information Maximum Likelihood Estimation: Estimation with Parameter Constraints

Seth Greenblatt ()

Computational Economics, 1995, vol. 8, issue 4, 267-81

Abstract: In this study, we take a method presented in an earlier paper, called a tensor method, and apply it to the computation of constrained FIML estimates. This technique 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 discuss interior and exterior point methods for constrained optimization, show how they can be used in conjunction with tensor methods, and then present test results showing that the tensor method is far more efficient than the standard Newton's method for a wide range of constrained FIML estimation problems. Citation Copyright 1995 by Kluwer Academic Publishers.

Date: 1995
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:8:y:1995:i:4:p:267-81

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().