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Inference about the Indirect Effect: a Likelihood Approach

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  • Noud P.A. van Giersbergen

Abstract

Prior research for constructing confidence intervals for an indirect effect has focused on a Wald statistic. In this paper, however, the inference problem is analyzed from a likelihood ratio (LR) perspective. When testing the null hypothesis $H_{0}:\ \alpha \beta =0$, the LR test statistic leads to the minimum of two t-ratios, whose size can be controlled. A confidence interval is obtained by inverting the LR statistic. Another confidence interval is obtained by inverting the sum of two pivotal t-statistics. In the Monte Carlo simulations, this latter confidence interval is the best performer: it outperforms the commonly used existing methods.

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  • Noud P.A. van Giersbergen, 2014. "Inference about the Indirect Effect: a Likelihood Approach," UvA-Econometrics Working Papers 14-10, Universiteit van Amsterdam, Dept. of Econometrics.
    • Handle: RePEc:ame:wpaper:1410
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    References listed on IDEAS

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    1. Michael Sobel, 1990. "Effect analysis and causation in linear structural equation models," Psychometrika, Springer;The Psychometric Society, vol. 55(3), pages 495-515, September.
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    Cited by:

    1. Oliver Hines & Stijn Vansteelandt & Karla Diaz-Ordaz, 2021. "Robust Inference for Mediated Effects in Partially Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 595-618, June.
    2. Kees Jan van Garderen & Noud van Giersbergen, 2020. "A Nearly Similar Powerful Test for Mediation," Papers 2012.11342, arXiv.org, revised Jan 2022.

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