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Estimation of Time-Varying Markov Processes with Aggregate Data

Elizabeth Chase MacRae

Abstract

The exact stochastic character of observed data from a Markov process is derived for the case where only aggregate stocks, as opposed to individual transitions, are observed. Particular attention is devoted to the distinction between data generated by a panel study, where a single group of individuals is followed over time, and that generated by random sampling, where the observed groups are not identical over time. Several alternative estimators are developed which take into account the particular stochastic structure of the data.