Estimation of high-dimensional seemingly unrelated regression models

References

• Baltagi, B. H., Bresson, G. (2011). Maximum likelihood estimation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: An application to hedonic housing prices in Paris. Journal of Urban Economics 69(1):24–42. doi:10.1016/j.jue.2010.08.007
   Web of Science ®Google Scholar
• Banerjee, A., Chandrasekhar, A. G., Duflo, E., Jackson, M. O. (2013). The diffusion of microfinance. Science (New York, N.Y.) 341(6144):1236498. doi:10.1126/science.1236498
   PubMed Web of Science ®Google Scholar
• Barabási, A.-L. (2016). Network science. Cambridge: Cambridge University Press.
   Google Scholar
• Bernstein, D. S. (2005). Matrix Mathematics: Theory, Facts, and Formulas with Application to Linear Systems Theory, Volume 41. Princeton: Princeton University Press.
   Google Scholar
• Bien, J., Tibshirani, R. J. (2011). Sparse estimation of a covariance matrix. Biometrika 98(4):807–820. doi:10.1093/biomet/asr054
   PubMed Web of Science ®Google Scholar
• Cai, T. T., Hu, J., Li, Y., Xinghua, Z. (2019). High-dimensional minimum variance portfolio estimation based on high-frequency data. Journal of Econometrics 214(2):482–494.
   Web of Science ®Google Scholar
• Cai, T., Liu, W., Luo, X. (2011). A constrained l 1 minimization approach to sparse precision matrix estimation. Journal of the American Statistical Association 106(494):594–607. doi:10.1198/jasa.2011.tm10155
   Web of Science ®Google Scholar
• Chiong, K. X., Moon, H. R. (2018). Estimation of graphical lasso using the l1, 2 norm. The Econometrics Journal 21(3):247–263. doi:10.1111/ectj.12104
   Web of Science ®Google Scholar
• Elberg, A. (2016). Sticky prices and deviations from the law of one price: Evidence from Mexican micro-price data. Journal of International Economics 98(C):191–203.
   Google Scholar
• Fan, M., Han, X., Pan, G., Jiang, B. (2019). Large system of seemingly unrelated regressions: A penalized quasi-maximum likelihood estimation perspective. Econometric Theory 27:1–33.
   Google Scholar
• Friedman, J., Hastie, T., Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics (Oxford, England) 9(3):432–441. doi:10.1093/biostatistics/kxm045
   PubMed Web of Science ®Google Scholar
• Greene, W. H. (2008). Econometric Analysis. 6 ed. Upper Saddle River, NJ: Pearson Prentice Hall.
   Google Scholar
• Hastie, T., Tibshirani, R., Wainwright, M. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press.
   Google Scholar
• Horn, R. A., Johnson, C. R. (1990). Matrix Analysis. Cambridge: Cambridge University Press.
   Google Scholar
• Johnstone, I. M., Lu, A. Y. (2009). On consistency and sparsity for principal components analysis in high dimensions. Journal of the American Statistical Association 104(486):682–693. doi:10.1198/jasa.2009.0121
   PubMed Web of Science ®Google Scholar
• Lam, C., Fan, J. (2009). Sparsistency and rates of convergence in large covariance matrix estimation. Annals of Statistics 37(6B):4254–4278. doi:10.1214/09-AOS720
   PubMed Web of Science ®Google Scholar
• Liu, H., Lafferty, J., Wasserman, L. (2009). The nonparanormal: Semiparametric estimation of high-dimensional undirected graphs. Journal of Machine Learning Research 10:2295–2328.
   Web of Science ®Google Scholar
• Loh, P., Wainwright, M. J. (2013). Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses. The Annals of Statistics 41(6):3022–3049. doi:10.1214/13-AOS1162
   Web of Science ®Google Scholar
• Pástor, L., Stambaugh, R. F. (2002). Mutual fund performance and seemingly unrelated assets. Journal of Financial Economics 63(3):315–349. doi:10.1016/S0304-405X(02)00064-8
   Web of Science ®Google Scholar
• Ravikumar, P., Wainwright, M. J., Raskutti, G., Yu, B. (2011). High-dimensional covariance estimation by minimizing l1-penalized log-determinant divergence. Electronic Journal of Statistics 5(0):935–980. doi:10.1214/11-EJS631
   Google Scholar
• Vershynin, R. (2018). High-Dimensional Probability: An Introduction with Applications in Data Science. Vol. 47. Cambridge: Cambridge University Press.
   Google Scholar
• Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association 57(298):348–368. doi:10.1080/01621459.1962.10480664
   Web of Science ®Google Scholar