Dear All,

I don't know whether I should ask the question here. It's the question
related to mixed-effects model, but not exactly R.

The question has been asked on StackExchange:Cross Validated too, please
see the link below

http://stats.stackexchange.com/questions/22323/fixed-effects-models-or-mixed-effects-models-to-population-based-data

Panel data analysis raised from econometrics deals with cross-sectional
time series data in a large scale, as counties, cities or states. As states
can hardly be considered from a "superpopulation", someone may argue
fixed-effects models may appropriate to this. However as my understanding
and perference to mixe-effects models,  time-invariant covariates are
treated as fixed-effect parameters, and time-varying covariates can be
divided into fixed-effects (population averaged) and random-effects
(subject/state-specifice effects), say "time" slope. As such, even for
state level longitudinal analysis for the population data, the
mixed-effects model can be quantified the heterogeneity in the trajectory
for each state.

Please correct me if anything stated is wrong, and I would like to hear
more opionions on this.

Thank you very much.

Fred

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