Lidan Tan, Khai X. Chiong, Hyungsik Roger Moon
arXiv 13 Nov 2018 · Econometrics · publishedEconometric Reviews (2021) · 1 citations (OpenAlex)
arXiv:1811.05567 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pradeep Ravikumar, Martin J Wainwright, Garvesh Raskutti, Bin Yu, et… (2011) High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence | 1.000 | 6 | 3 | 100% |
| 2 | Jerome Friedman, Trevor Hastie, and Robert Tibshirani (2008) Sparse inverse covariance estimation with the graphical lasso | 0.843 | 3 | 3 | 100% |
| 3 | Arnold Zellner (1962) An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias | 0.843 | 3 | 3 | 100% |
| 4 | Albert-László Barabási et al (2016) Network science | 0.644 | 2 | 2 | 100% |
| 5 | William H Greene (2003) Econometric analysis | 0.644 | 2 | 2 | 100% |
| 6 | Sahand Negahban and Martin J Wainwright (2011) Estimation of (near) low-rank matrices with noise and high-dimensional scaling | 0.644 | 2 | 2 | 100% |
| 7 | Roman Vershynin (2018) High-dimensional probability: An introduction with applications in data science, volume 47 | 0.511 | 2 | 2 | 50% |
| 8 | Trevor Hastie, Robert Tibshirani, and Martin Wainwright (2015) Statistical learning with sparsity: the lasso and generalizations | 0.511 | 2 | 1 | 100% |
| 9 | Tony Cai, Weidong Liu, and Xi Luo (2011) A constrained ℓ 1 minimization approach to sparse precision matrix estimation | 0.405 | 1 | 1 | 100% |
| 10 | Jianqing Fan, Yuan Liao, and Han Liu (2016) An overview of the estimation of large covariance and precision matrices | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 15 scored citations.