Sparse High-Dimensional Models in Economics
综述稀疏高维模型的理论、方法和实现，重点介绍惩罚最小二乘和惩罚似然方法在经济学和金融中的应用，帮助研究者了解变量选择技术在高维稀疏建模中的有效性。
This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in ultra-high dimensional sparse modeling are also briefly discussed.