Using double sampling to correct gross flows for misclassification error: moment-based inference vs. likelihood-based inference

Longitudinal surveys provide a key source of information for analyzing dynamic phenomena. Typical examples of longitudinal data are gross flows between a finite number of states. Sample surveys are, however, affected by nonsampling errors. We investigate the use of double sampling for correcting discrete longitudinal data for misclassification error. In a double sampling context, we assume that along with the main measurement device, which is affected by misclassification error, we can use a secondary measurement device, which is free of error but more expensive to apply. Inference is based on combining information from both measurement devices. Traditional moment-based inference is reviewed and contrasted, under alternative double sampling schemes, with a proposed likelihood-based method that works by simultaneously modeling the true transition process and the measurement error process within the context of a missing data problem. Variance estimation, under both approaches, is discussed. Monte Carlo simulation experiments indicate that the proposed likelihood-based method offers significant gains in efficiency over the traditional moment-based method