摘要翻译：
在本文中，我们研究了看似无关回归(SUR)模型，它允许方程(N)的数目很大，并且与每个方程(T)中的观测值的数目相当。文献中众所周知，传统的SUR估计，例如Zellner(1962)的广义最小二乘(GLS)估计不能很好地执行。作为本文的主要贡献，我们提出了一种新的可行GLS估计，称为可行图形lasso（FGLasso）估计。对于GLS估计器的可行实现，我们使用精度矩阵（方程组系统误差的协方差矩阵的逆）的图形lasso估计，假设潜在的未知精度矩阵是稀疏的。我们推导了新估计量的渐近理论，并通过Monte-Carlo模拟研究了它的有限样本性质。
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英文标题：
《Estimation of High-Dimensional Seemingly Unrelated Regression Models》
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作者：
Lidan Tan, Khai X. Chiong, Hyungsik Roger Moon
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  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 Monte-Carlo simulations.
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PDF链接：
https://arxiv.org/pdf/1811.05567