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We present a stochastic model of population dynamics exploitingcross-sectional data in trend analysis and forecasts for groups and cohorts ofa population. While sharing the convenient features of classic Markov models,it alleviates the practical problems experienced in longitudinal studies. Basedon statistical and information-theoretical analysis, we adopt maximumlikelihood estimation to determine model parameters, facilitating the use of arange of model selection methods. Their application to several synthetic andempirical datasets shows that the proposed approach is robust, stable andsuperior to a regression-based one. We extend the basic framework to simulateageing cohorts, processes with finite memory, distinguishing their short andlong-term trends, introduce regularisation to avoid the ecological fallacy, andgeneralise it to mixtures of cross-sectional and (possibly incomplete)longitudinal data. The presented model illustrations yield new and interestingresults, such as an implied common driving factor in obesity for allgenerations of the English population and "yo-yo" dieting in the U.S. data.

Cross-sectional Markov model for trend analysis of observed discrete distributions of population characteristics