Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6770
Title: Estimating seemingly unrelated regression models with vector autoregressive disturbances
Authors: Foschi, Paolo 
Kontoghiorghes, Erricos John 
Keywords: Least squares;Analysis of variance
Issue Date: 2003
Publisher: Elsevier
Source: Journal of Economic Dynamics and Control, 2003, Volume 28, Issue 1, Pages 27-44
Abstract: The numerical solution of seemingly unrelated regression (SUR) models with vector autoregressive disturbances is considered. Initially, an orthogonal transformation is applied to reduce the model to one with smaller dimensions. The transformed model is expressed as a reduced-size SUR model with stochastic constraints. The generalized QR decomposition is used as the main computational tool to solve this model. An iterative estimation algorithm is proposed when the variance-covariance matrix of the disturbances and the matrix of autoregressive coefficients are unknown. Strategies to compute the orthogonal factorizations of the non-dense-structured matrices which arise in the estimation procedure are presented. Experimental results demonstrate the computational efficiency of the proposed algorithm.
URI: http://ktisis.cut.ac.cy/handle/10488/6770
ISSN: 01651889
DOI: http://dx.doi.org/10.1016/S0165-1889(02)00105-7
Rights: © 2002 Elsevier Science B.V. All rights reserved.
Type: Article
Appears in Collections:Άρθρα/Articles

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