Estimations and Tests for Generalized Mediation Models with High-Dimensional Potential Mediators

Xu Guo, Runze Li, Jingyuan Liu, Mudong Zeng

Research output: Contribution to journalArticlepeer-review

Abstract

Motivated by an empirical analysis of stock reaction to COVID-19 pandemic, we propose a generalized mediation model with high-dimensional potential mediators to study the mediation effects of financial metrics that bridge company’s sector and stock value. We propose an estimation procedure for the direct effect via a partial penalized maximum likelihood method and establish its theoretical properties. We develop a Wald test for the indirect effect and show that the proposed test has a (Formula presented.) limiting null distribution. We also develop a partial penalized likelihood ratio test for the direct effect and show that the proposed test asymptotically follows a (Formula presented.) -distribution under null hypothesis. A more efficient estimator of indirect effect under complete mediation model is also developed. Simulation studies are conducted to examine the finite sample performance of the proposed procedures and compare with some existing methods. We further illustrate the proposed methodology with an empirical analysis of stock reaction to COVID-19 pandemic via exploring the underlying mechanism of the relationship between companies’ sectors and their stock values.

Original languageEnglish (US)
Pages (from-to)243-256
Number of pages14
JournalJournal of Business and Economic Statistics
Volume42
Issue number1
DOIs
StatePublished - 2024

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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