Abstract
Computationally efficient and numerically stable methods for solving Seemingly Unrelated Regression Equations (SURE) models are proposed. The iterative feasible generalized least squares estimator of SURE models where the regression equations have common exogenous variables is derived. At each iteration an estimator of the SURE model is obtained from the solution of a generalized linear least squares problem. The proposed methods, which have as a basic tool the generalized QR decomposition, are also found to be efficient in the general case where the number of linear independent regressors is smaller than the number of observations.
This work is in part supported by the Swiss National Foundation Grants 21-54109.98 and 1214-056900.99/1.
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Kontoghiorghes, E.J., Foschi, P. (2001). Computationally E.cient Methods for Solving SURE Models. In: Vulkov, L., Yalamov, P., Waśniewski, J. (eds) Numerical Analysis and Its Applications. NAA 2000. Lecture Notes in Computer Science, vol 1988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45262-1_57
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DOI: https://doi.org/10.1007/3-540-45262-1_57
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