期刊
JOURNAL OF ECONOMETRICS
卷 218, 期 1, 页码 119-139出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2019.12.016
关键词
Conditional dependence; Distance covariance; Factor model; Graphical model; Projection
资金
- National Science Foundation, USA grant NSF grant [DMS-1308566]
- National Science Foundation, USA grant NSF CAREER grant [DMS-1554804]
- National Science Foundation, USA grant NSF [DMS-1662139, DMS-1712591]
- National Institutes of Health, USA grant [R01-GM072611-14]
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. (C) 2020 Elsevier B.V. All rights reserved.
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