Estimation of high-dimensional seemingly unrelated regression models

Lidan Tan, Khai Xiang Chiong, Hyungsik Roger Moon

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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, and it shares the same asymptotic distribution with the efficient GLS estimator when (Formula presented.) We confirm these results through finite sample Monte-Carlo simulations.

Original languageEnglish
Pages (from-to)830-851
Number of pages22
JournalEconometric Reviews
Issue number9
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2021 Taylor & Francis Group, LLC.

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


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