Seemingly unrelated regression equations models
Seemingly unrelated regression equations models
Structured computer organization (3rd ed.)
Structured computer organization (3rd ed.)
Computational Statistics & Data Analysis
Displacement structure: theory and applications
SIAM Review
Matrix computations (3rd ed.)
Stability Issues in the Factorization of Structured Matrices
SIAM Journal on Matrix Analysis and Applications
Kronecker Matrices, Computer Implementation, and Generalized Spectra
Journal of the ACM (JACM)
Computational Economics - Computational Studies at Stanford
Numerical Linear Algebra for High Performance Computers
Numerical Linear Algebra for High Performance Computers
Computationally Efficient Methods for Solving SURE Models
NAA '00 Revised Papers from the Second International Conference on Numerical Analysis and Its Applications
Computationally efficient methods for estimating the updated-observations SUR models
Applied Numerical Mathematics
Computational Statistics & Data Analysis
Message-passing two steps least square algorithms for simultaneous equations models
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Seemingly unrelated regression model with unequal size observations: computational aspects
Computational Statistics & Data Analysis
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The Vector Autoregressive (VAR) model with zero coefficient restrictions canbe formulated as a Seemingly Unrelated Regression Equation (SURE) model. Boththe response vectors and the coefficient matrix of the regression equationscomprise columns from a Toeplitz matrix. Efficient numerical and computationalmethods which exploit the Toeplitz and Kronecker product structure of thematrices are proposed. The methods are also adapted to provide numericallystable algorithms for the estimation of VAR(p) models with Granger-causedvariables.