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Cited by 384

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects

Open access
Allison, Paul D., Williams, Richard, Moral-Benito, Enrique·2017·Socius: Sociological Research for a Dynamic World

Abstract

Panel data make it possible both to control for unobserved confounders and allow for lagged, reciprocal causation. Trying to do both at the same time, however, leads to serious estimation difficulties. In the econometric literature, these problems have been solved by using lagged instrumental variables together with the generalized method of moments (GMM). Here we show that the same problems can be solved by maximum likelihood (ML) estimation implemented with standard software packages for structural equation modeling (SEM). Monte Carlo simulations show that the ML-SEM method is less biased and more efficient than the GMM method under a wide range of conditions. ML-SEM also makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models.

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Metadata

Authors
Allison, P. D., Williams, R., & Moral-Benito, E.
Venue
Socius: Sociological Research for a Dynamic World
Published
2017-01-01
Volume
3
ISSN
2378-0231 (ISSN-L)
OpenAlex
W2624790950
Cited by
384
References
45
FWCI
41.99
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects. https://app.cochat.ai/papers/10.1177/2378023117710578
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Allison, P. D., Williams, R., & Moral-Benito, E. (2017). Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects. Socius: Sociological Research for a Dynamic World. https://doi.org/10.1177/2378023117710578

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.