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

Jianqing Fan, Yang Feng, Lucy Xia

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)119-139
Number of pages21
JournalJournal of Econometrics
Volume218
Issue number1
DOIs
StatePublished - Sep 2020

Keywords

  • Conditional dependence
  • Distance covariance
  • Factor model
  • Graphical model
  • Projection

ASJC Scopus subject areas

  • Economics and Econometrics

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