Estimation of graphical models using the L1,2 norm

Khai Xiang Chiong, Hyungsik Roger Moon

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1 Citation (Scopus)


Gaussian graphical models are recently used in economics to obtain networks of dependence among agents. A widely used estimator is the graphical least absolute shrinkage and selection operator (GLASSO), which amounts to a maximum likelihood estimation regularized using the L1,1 matrix norm on the precision matrix Ω. The L1,1 norm is a LASSO penalty that controls for sparsity, or the number of zeros in Ω. We propose a new estimator called structured GLASSO (SGLASSO) that uses the L1,2 mixed norm. The use of the L1,2 penalty controls for the structure of the sparsity in Ω. We show that when the network size is fixed, SGLASSO is asymptotically equivalent to an infeasible GLASSO problem which prioritizes the sparsity-recovery of high-degree nodes. Monte Carlo simulation shows that SGLASSO outperforms GLASSO in terms of estimating the overall precision matrix and in terms of estimating the structure of the graphical model. In an empirical illustration using a classic firms' investment data set, we obtain a network of firms' dependence that exhibits the core–periphery structure, with General Motors, General Electric and US Steel forming the core group of firms.

Original languageEnglish
Pages (from-to)247-263
Number of pages17
JournalEconometrics Journal
Issue number3
Publication statusPublished - 2018 Oct

Bibliographical note

Funding Information:
We thank Victor Chernozhukov and two referees for helpful comments and suggestions. This paper was written while K. X. Chiong was a postdoctoral fellow at USC Dornsife INET, and H. R. Moon was the Associate Director of USC Dornsife INET. H. R. Moon acknowledges that this work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A5A2A01023679).

Publisher Copyright:
© 2017 Royal Economic Society

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


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