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.
Kontoghiorghes, E.J., Foschi, P. (2001). Computationally efficient methods for solving SURE models. Springer Nature [10.1007/3-540-45262-1_57].
Computationally efficient methods for solving SURE models
Foschi P.
2001
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
