Publication: Estimating Latent-Variable Panel Data Models Using Parameter-Expanded SEM Methods
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Date
2024-07-15
Authors
Wei, Siqi
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Court
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Taylor & Francis
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Abstract
This article presents new estimation algorithms for three types of dynamic panel data models with latentvariables: factor models, discrete choice models, and persistent-transitory quantile processes. The newmethods combine the parameter expansion (PX) ideas of Liu, Rubin, and Wu with the stochastic expectation-maximization (SEM) algorithm in likelihood and moment-based contexts. The goal is to facilitate conver-gence in models with a large space of latent variables by improving algorithmic efficiency. This is achieved byspecifying expanded models within the M step. Effectively, we are proposing new estimators for the pseudo-data within iterations that take into account the fact that the model of interest is misspecified for drawsbased on parameter values far from the truth. We establish the asymptotic equivalence of the likelihood-based PX-SEM to an alternative SEM algorithm with a smaller expected fraction of missing informationcompared to the standard SEM based on the original model, implying a faster global convergence rate.Finally, in simulations we show that the new algorithms significantly improve the convergence speed relativeto standard SEM algorithms, sometimes dramatically so, by reducing the total computing time from hoursto a few minutes.
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Attribution-NonCommercial-NoDerivatives 4.0 International
School
IE Business School
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Citation
Wei, S. (2024). Estimating Latent-Variable Panel Data Models Using Parameter-Expanded SEM Methods. Journal of Business & Economic Statistics, 1-14. https://doi.org/10.1080/07350015.2024.2365783.