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Estimation of VAR Models Computational Aspects

  • Paolo Foschi
  • Erricos Kontoghiorghes

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. Copyright Kluwer Academic Publishers 2003

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File URL: http://hdl.handle.net/10.1023/A:1022281319272
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Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 21 (2003)
Issue (Month): 1 (February)
Pages: 3-22

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Handle: RePEc:kap:compec:v:21:y:2003:i:1:p:3-22
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  1. Kontoghiorghes, E. J. & Clarke, M. R. B., 1995. "An alternative approach for the numerical solution of seemingly unrelated regression equations models," Computational Statistics & Data Analysis, Elsevier, vol. 19(4), pages 369-377, April.
  2. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501, March.
  3. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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