Tensor methods for full-information maximum likelihood estimation: Unconstrained estimation

DOI10.1007/BF01299569zbMath0810.62101OpenAlexW1993019982MaRDI QIDQ1342438

Seth A. Greenblatt

Publication date: 25 April 1995

Published in: Computational Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01299569

zbMATH Keywords

Newton's methodnonlinear modelstensor methodfourth order approximation to the log-likelihood functionLucas- Rapping modelunconstrained full-information maximum likelihood estimates

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Numerical approximation and computational geometry (primarily algorithms) (65D99) Probabilistic methods, stochastic differential equations (65C99)

Related Items (1)

Tensor methods of full-information maximum likelihood estimation: Estimation with parameter constraints

Cites Work

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• Tensor Methods for Nonlinear Equations
• An Algorithm for FIML and 3SLS Estimation of Large Nonlinear Models
• Tensor Methods for Unconstrained Optimization Using Second Derivatives
• The Statistical Implications of a System of Simultaneous Equations

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