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Estimating seemingly unrelated regression models with vector autoregressive disturbances

โœ Scribed by Paolo Foschi; Erricos J. Kontoghiorghes

Publisher
Elsevier Science
Year
2003
Tongue
English
Weight
341 KB
Volume
28
Category
Article
ISSN
0165-1889

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โœฆ Synopsis

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 coe cients 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 e ciency of the proposed algorithm.

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