A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models

Jianqing Fan, Yang Feng and Lucy Xia

Journal of Econometrics, 2020, vol. 218, issue 1, 119-139

Abstract: 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.

Keywords: Conditional dependence; Distance covariance; Factor model; Graphical model; Projection (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:218:y:2020:i:1:p:119-139

DOI: 10.1016/j.jeconom.2019.12.016

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