Estimation of Growth Curve Models with Structured Error Covariances by Generalized Estimating Equations

The growth curve model is useful for the analysis of longitudinal data. It helps investigate an overall pattern of change in repeated measurements over time and the effects of time-invariant explanatory variables on the temporal pattern. The traditional growth curve model assumes that the matrix of covariances between repeated measurements is unconstrained. This unconstrained covariance matrix often appears unattractive. In this paper, the generalized estimating equation method is adopted to estimate parameters of the growth curve model. As a result, the proposed method allows a more variety of constrained covariance structures than the traditional growth curve model. An empirical application is provided so as to illustrate the proposed method.

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