IDENTIFICATION THEORY FOR VARYING COEFFICIENT REGRESSION MODELS

DOI10.1111/j.1467-9892.1987.tb00447.xzbMath0625.62077OpenAlexW2021437537MaRDI QIDQ3028144

Kent D. Wall

Publication date: 1987

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.1987.tb00447.x

zbMATH Keywords

identification problemequivalence classesregression modelstime-varying coefficientsARMAlinear transformationsstate-space representationobservational equivalencelinear dynamic systemsadmissible transformationnon-stationary casesecond moment informationsecond moments structure

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Identification in stochastic control theory (93E12)

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Cites Work

• Unnamed Item
• Unnamed Item
• Simultaneous equations models in applied search theory
• Estimation, filtering, and smoothing in state space models with incompletely specified initial conditions
• Local and global identification and strong consistency in time series models
• Some identification and estimation results for regression models with stochastically varying coefficients
• Kalman filtering estimation of unobserved rational expectations with an application to the German hyperinflation
• The determination of state-space representations for linear multivariable systems
• Affine Feedback Controllability of Constant Coefficient Differential Equations
• Identification Results for Armax Structures
• Canonical forms for the identification of multivariable linear systems
• Parametrizations of linear dynamical systems: Canonical forms and identifiability
• On the Global Identification of the Dynamic Simultaneous Equations Model with Stationary Disturbances
• The Identification and Parameterization of Armax and State Space Forms
• Linear system identification from nonstationary cross-sectional data
• The Theory of Parametric Identification
• The Synthesis of Linear Dynamical Systems from Prescribed Weighting Patterns
• Identification in Parametric Models
• The Identification Problem for Multiple Equation Systems with Moving Average Errors