Diagnostics for use with regression recursive residuals
Technometrics
Estimation of VAR Models: Computational Aspects
Computational Economics
Computational methods for modifying seemingly unrelated regressions models
Journal of Computational and Applied Mathematics - Special issue: Proceedings of the international conference on linear algebra and arithmetic, Rabat, Morocco, 28-31 May 2001
The specification of rank reducing observation sets in experimental design
Computational Statistics & Data Analysis
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The use of residuals for detecting departures from the assumptions of the linear model with full-rank covariance, whether the design matrix is full rank or not, has long been recognized as an important diagnostic tool. Once it became feasible to compute different kinds of residual in a straight forward way, various methods have focused on their underlying properties and their effectiveness. The recursive residuals are attractive in Econometric applications where there is a natural ordering among the observations through time. New formulations for the recursive residuals for models having uncorrelated errors with equal variances are given in terms of the observation vector or the usual least-squares residuals, which do not require the computation of least-squares parameter estimates and for which the transformation matrices are expressed wholly in terms of the rows of the Theil Z matrix. Illustrations of these new formulations are given.