Maximum Likelihood for Cross-lagged Panel Models with Fixed Effects

By P. Allison, Richard Williams, Enrique Moral-Benito. Panel Data Econometrics, 2017

326 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/4769951e6951393d2de7d506258ebd024b1465ed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Lars Leszczensky
2. Tobias Wolbring
3. P. Allison
4. William H. Greene
5. Quan Li

Citations in the corpus

• How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study (2019, methodology): “Following Allison et al. (2017), Figure 1 illustrates the model for T¼ 4.”
• Dynamic panel data modelling using maximum likelihood: an alternative to Arellano-Bond (2018, unknown): “The widely used first-differenced GMM estimator discussed in Arellano and Bond (1991) may suffer from finite sample biases when the number of crosssection observations is small.”
• Asymmetric Fixed-effects Models for Panel Data (2019, methodology): “A final attraction is that this approach can be incorporated into the structural equation model panel methods proposed by Allison, Williams, and Moral-Benito (2017), which make it possible to relax many of the hidden constraints of conventional fixedeffects models.”
• Limitations of Fixed-Effects Models for Panel Data (2020, background): “If the coefficients for supposed time-invariant characteristics vary with time, they become equivalent to time-varying characteristics (Allison, Williams, and Moral-Benito 2017).”