Identification and estimation of dynamic errors-in-variables models

DOI10.1016/0304-4076(89)90046-8zbMath0692.62073OpenAlexW2092247018MaRDI QIDQ583796

Anthony M. Bloch

Publication date: 1989

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4076(89)90046-8

zbMATH Keywords

maximum likelihood estimationGrassmann manifoldIdentificationstationary time seriesmaximizationnonuniquenessARMAX modelslinear dynamic errors-in-variable modelstotal least squares model

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Cites Work

• The weak and strong Gaussian probabilistic realization problem
• Rank-reducibility of a symmetric matrix and sampling theory of minimum trace factor analysis
• Estimation, principal components and Hamiltonian systems
• Some contributions to maximum likelihood factor analysis
• Dynamic Factor-Analysis Models for Stationary Processes
• Consistent Sets of Estimates for Regressions with Errors in All Variables
• IDENTIFIABILITY IN DYNAMIC ERRORS-IN-VARIABLES MODELS
• Multivariable Nyquist criteria, root loci, and pole placement: A geometric viewpoint
• Convexity and Commuting Hamiltonians
• VI.—The Estimation of Factor Loadings by the Method of Maximum Likelihood
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