Estimating regressions and seemingly unrelated regressions with error component disturbances

Paolo Foschi

MPRA Paper from University Library of Munich, Germany

Abstract: The estimation of regressions models with two-way error component disurbances, is considered for the case where both the random effects are non-spherically distributed. The usual approach that first transforms the effects into uncorrelated ones and then applies within and between transformations, cannot be conveniently applied. Here, it is proposed to revert this scheme by firstly applying the within and between transformations. This results in simple General Linear Model which can be partitioned into three smaller GLMs. Then, by exploiting the structure of the models and using the Generalized QR decomposition as a tool, a computationally efficient and numerically reliable method for estimating the regression parameters is derived. This estimation method is generalized to the case of a system of seemingly unrelated regressions.

Keywords: panel data models; regressions; seemingly unrelated regressions; generalized least-squares; error components; orthogonal transformation; numerical methods (search for similar items in EconPapers)
JEL-codes: C32 C33 C63 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2005-02-08, Revised 2006-09-07
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Persistent link: http://EconPapers.repec.org/RePEc:pra:mprapa:1424

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