Estimation of high-dimensional seemingly unrelated regression models
Khai Xiang Chiong and
Hyungsik Roger Moon
Econometric Reviews, 2021, vol. 40, issue 9, 830-851
In this article, we investigate seemingly unrelated regression (SUR) models that allow the number of equations (N) to be large and comparable to the number of the observations in each equation (T). It is well known that conventional SUR estimators, for example, the feasible generalized least squares estimator from Zellner (1962) does not perform well in a high-dimensional setting. We propose a new feasible GLS estimator called the feasible graphical lasso (FGLasso) estimator. For a feasible implementation of the GLS estimator, we use the graphical lasso estimation of the precision matrix (the inverse of the covariance matrix of the equation system errors) assuming that the underlying unknown precision matrix is sparse. We show that under certain conditions, FGLasso converges uniformly to GLS even when T N log N. We confirm these results through finite sample Monte-Carlo simulations.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:40:y:2021:i:9:p:830-851
Ordering information: This journal article can be ordered from
Access Statistics for this article
Econometric Reviews is currently edited by Dr. Essie Maasoumi
More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().