Estimation and inference in SUR models when the number of equations is large

Fiebig, Denzil G., and Kim, Jae H. (2000) Estimation and inference in SUR models when the number of equations is large. Econometric Reviews, 19 (1). pp. 105-130.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website:


There is a tendency for the true variability of feasible GLS estimators to be understated by asymptotic standard errors. For estimation of SUR models, this tendency becomes more severe in large equation systems when estimation of the error covariance matrix, C, becomes problematic. We explore a number of potential solutions involving the use of improved estimators for the disturbance covariance matrix and bootstrapping. In particular, Ullah and Racine (1992) have recently introduced a new class of estimators for SUR models that use nonparametric kernel density estimation techniques. The proposed estimators have the same structure as the feasible GLS estimator of Zellner (1962) differing only in the choice of estimator for C. Ullah and Racine (1992) prove that their nonparametric density estimator of C can be expressed as Zellner's original estimator plus a positive definite matrix that depends on the smoothing parameter chosen for the density estimation. It is this structure of the estimator that most interests us as it has the potential to be especially useful in large equation systems.

Atkinson and Wilson (1992) investigated the bias in the conventional and bootstrap estimators of coefficient standard errors in SUR models. They demonstrated that under certain conditions the former were superior, but they caution that neither estimator uniformly dominated and hence bootstrapping provides little improvement in the estimation of standard errors for the regression coefficients. Rilstone and Veal1 (1996) argue that an important qualification needs to be made to this somewhat negative conclusion. They demonstrated that bootstrapping can result in improvements in inferences if the procedures are applied to the t-ratios rather than to the standard errors. These issues are explored for the case of large equation systems and when bootstrapping is combined with improved covariance estimation.

Item ID: 12848
Item Type: Article (Research - C1)
ISSN: 1532-4168
Keywords: genes; SUR models; seemingly unrelated regression models; improved covariance estimation; bootstrapping; large equation systems
Additional Information:

A preliminary version of this paper was presented at the Econometric Society Australasian Meeting held in Melbourne, July 1997.

Funders: Australian Research Council (ARC)
Projects and Grants: ARC MEMLAB, ARC Sydney VisLab
Date Deposited: 16 Feb 2017 05:43
FoR Codes: 14 ECONOMICS > 1403 Econometrics > 140303 Economic Models and Forecasting @ 100%
SEO Codes: 91 ECONOMIC FRAMEWORK > 9102 Microeconomics > 910299 Microeconomics not elsewhere classified @ 100%
Downloads: Total: 5
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page