Estimations and Tests for Generalized Mediation Models with High-Dimensional Potential Mediators
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.
Files
Metadata
Work Title | Estimations and Tests for Generalized Mediation Models with High-Dimensional Potential Mediators |
---|---|
Access | |
Creators |
|
Keyword |
|
License | In Copyright (Rights Reserved) |
Work Type | Article |
Publisher |
|
Publication Date | March 3, 2023 |
Publisher Identifier (DOI) |
|
Deposited | October 07, 2024 |
Versions
Analytics
Collections
This resource is currently not in any collection.