THE ESTIMATION OF GROSS FLOWS IN THE PRESENCE OF MEASUREMENT ERROR USING AUXILIARY VARIABLES

Classification error can lead to substantial biases in the estimation of gross flows from longitudinal data. We propose a method to adjust f low estimates for bias, based on fitting separate multinomial logistic models to the classification error probabilities and the true state t ransition probabilities using values of auxiliary variables. Our appro ach has the advantages that it does not require external information o n misclassification rates, it permits the identification of factors th at are related to misclassification and true transitions and it does n ot assume independence between classification errors at successive poi nts in time. Constraining the prediction of the stocks to agree with t he observed stocks protects against model misspecification. We apply t he approach to data on women from the Panel Study of Income Dynamics w ith three categories of labour force status. The model fitted is shown to have interpretable coefficient estimates and to provide a good fit . Simulation results indicate good performance of the model in predict ing the true flows and robustness against departures from the model po stulated.