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Published on: October 13, 2018
Analysis of panel data under hidden mover-stayer models
Grace Y Yi1, Wenqing He2, Feng He1
1Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, N2L 3G1, Ontario, Canada.
Abstract:
Analysis of panel data is often challenged by the presence of heterogeneity and state misclassification. In this paper, we propose a hidden mover-stayer model to facilitate heterogeneity for a population that consists of two subpopulations each of movers or of stayers and to simultaneously account for state misclassification. We develop an inference procedure based on the expectation-maximization algorithm by treating the mover-stayer indicator and underlying true states as latent variables. We evaluate the performance of the proposed method and investigate the impact of ignoring misclassification through simulation studies. The proposed method is applied to analyze the data arising from the Waterloo Smoking Prevention Project. Copyright © 2017 John Wiley & Sons, Ltd.
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