Fan Jianqing, Feng Yang, Xia Lucy
Department of Operations Research & Financial Engineering, Princeton University, Princeton, NJ 08544, USA.
Department of Biostatistics, College of Global Public Health, New York University, New York, NY 10003, USA.
J Econom. 2020 Sep;218(1):119-139. doi: 10.1016/j.jeconom.2019.12.016. Epub 2020 Feb 15.
Measuring conditional dependence is an important topic in econometrics with broad applications including graphical models. Under a factor model setting, a new conditional dependence measure based on projection is proposed. The corresponding conditional independence test is developed with the asymptotic null distribution unveiled where the number of factors could be high-dimensional. It is also shown that the new test has control over the asymptotic type I error and can be calculated efficiently. A generic method for building dependency graphs without Gaussian assumption using the new test is elaborated. We show the superiority of the new method, implemented in the R package pgraph, through simulation and real data studies.
测量条件依赖性是计量经济学中的一个重要课题，具有包括图形模型在内的广泛应用。在因子模型设定下，提出了一种基于投影的新的条件依赖性度量。开发了相应的条件独立性检验，并揭示了其渐近零分布，其中因子数量可能是高维的。还表明，新检验能够控制渐近第一类错误，并且可以高效计算。阐述了一种使用新检验构建无高斯假设的依赖图的通用方法。通过模拟和实际数据研究，我们展示了在R包pgraph中实现的新方法的优越性。
