Computational methods for modifying seemingly unrelated regressions models

Abstract

Computational efficient methods for updating seemingly unrelated regressions models with new observations are proposed. A recursive algorithm to solve a series of updating problems is developed. The algorithm is based on orthogonal transformations and has as main computational tool the updated generalized QR decomposition (UGQRD). Strategies to compute the orthogonal factorizations by exploiting the block-sparse structure of the matrices are designed. The problems of adding and deleting exogenous variables from the seemingly unrelated regressions model have also been investigated. The solution of these problems utilize the strategies for computing the UGQRD.