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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.

Statistics in Medicine
|August 3, 2017
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Summary
This summary is machine-generated.

This study introduces a hidden mover-stayer model to address heterogeneity and misclassification in panel data analysis. The method accurately identifies population subgroups and underlying states, improving data interpretation.

Keywords:
EM algorithmhidden mover-stayer modelmisclassificationpanel data

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Panel data analysis frequently encounters challenges due to population heterogeneity and state misclassification.
  • Existing models may not adequately address both issues simultaneously, potentially leading to biased results.

Purpose of the Study:

  • To propose a novel hidden mover-stayer model to simultaneously account for heterogeneity and state misclassification in panel data.
  • To develop an effective inference procedure for the proposed model.
  • To evaluate the model's performance and the consequences of ignoring misclassification.

Main Methods:

  • Development of a hidden mover-stayer model distinguishing between 'movers' and 'stayers' subpopulations.
  • Application of the expectation-maximization algorithm for parameter estimation, treating mover-stayer status and true states as latent variables.
  • Simulation studies to assess model performance and the impact of unaddressed misclassification.

Main Results:

  • The proposed hidden mover-stayer model effectively handles heterogeneity and state misclassification in panel data.
  • Simulation results demonstrate the model's robustness and highlight the potential biases introduced by ignoring misclassification.
  • The model successfully analyzes real-world data from the Waterloo Smoking Prevention Project.

Conclusions:

  • The hidden mover-stayer model provides a robust framework for analyzing panel data with unobserved heterogeneity and state misclassification.
  • Accurate modeling of these complexities is crucial for reliable inference in various fields, including public health and social sciences.
  • The developed method offers a valuable tool for researchers dealing with complex longitudinal data structures.