Estimation of HighDimensional Seemingly Unrelated Regression Models
Abstract
In this paper, we investigate seemingly unrelated regression (SUR) models that allow the number of equations (N) to be large, and to be comparable to the number of the observations in each equation (T). It is well known in the literature that the conventional SUR estimator, for example, the generalized least squares (GLS) estimator of Zellner (1962) does not perform well. As the main contribution of the paper, 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 derive asymptotic theories of the new estimator and investigate its finite sample properties via MonteCarlo simulations.
 Publication:

arXiv eprints
 Pub Date:
 November 2018
 arXiv:
 arXiv:1811.05567
 Bibcode:
 2018arXiv181105567T
 Keywords:

 Economics  Econometrics