Recovering Transitions From Repeated Cross-Sectional Samples

Abstract

This paper proposes a dynamic Markov model for the estimation of binary state-to-state transition probabilities from a sequence of independent cross-sectional samples. It discusses parameter estimation and inference using maximum likelihood (ML) methodology. The model is illustrated by the application of a three-wave panel study on pupils’ interest in learning physics. These data encompass more information than what is used to estimate the model, but this surplus information allows us to assess the accuracy and the precision of the transition estimates. Bootstrap and Bayesian simulations are used to evaluate the accuracy and the precision of the ML estimates. To mimic genuine cross-sectional data, samples of independent observations randomly drawn from the panel are also analyzed.