Statistical Modelling 1 (2001), 271–285
A comparison of GEE and
random effects models for distinguishing heterogeneity, nonstationarity and
state dependence in a collection of short binary event series
R. Crouchley and R. B. Davies
Centre for Applied Statistics,
Lancaster University, England
email: r.crouchley@lancaster.ac.uk
Abstract:
GEE transition
models and Markov random effect models are applied to a simple panel data set
on depression. In each case, the precise specifications adopted were derived
from the authors' interpretation of best practice in the literature. The two
approaches result in quite different inference on the three process
characteristics of interest: state dependence, heterogeneity, and
nonstationarity. The design of the analyses permits indirect goodness of fit
measures to be derived for the GEE models and these indicate serious
deficiencies in this approach. It is shown through simulation and further
analyses of the depression data that these deficiencies may be corrected by
including the initial observation properly in the analyses and by adopting an
appropriate variance-covariance structure. The former problem is widely
understood in random effects modelling and is relatively straightforward to
address within GEE. The latter problem is more difficult because, without model
selection or goodness of fit measures generally available for GEE models, it is
not clear how one may select empirically between alternative
variance-covariance structures. Inappropriate variance-covariance
specifications prejudice consistent estimation of state dependence and
nonstationarity.
Keywords:
GEE; panel data; random effects; state dependence; transition model.
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