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This study compares state space models (estimated with the Kalman filter with a frequentist approach to hyperparameter estimation) with multilevel time series models (based on the hierarchical Bayesian framework). The application chosen is the Dutch Travel Survey featuring small sample sizes and discontinuities caused by the survey redesigns. Both modelling approaches deliver similar point and variance estimates. Slight differences in model-based variance estimates appear mostly in small-scaled domains and are due to neglecting uncertainty around the hyperparameter estimates in the state space models, and to a lesser extent to skewness in the posterior distributions of the parameters of interest. The results suggest that the reduction in design-based standard errors with the hierarchical Bayesian approach is over 50% at the provincial level, and over 30% at the national level. [ABSTRACT FROM AUTHOR]

During redesigns of repeated surveys, the old and new approaches are often conducted in parallel to quantify discontinuities that are initiated by modifications in the survey process. For budget limitations, the sample size allocated to the alternative approach is often considerably smaller compared with the regular survey that is used for official publication. In this paper, small area estimation techniques are considered to improve the accuracy of domain estimates obtained under the alternative approach. Besides auxiliary information that is available from administrations, direct domain estimates available from the regular survey are useful auxiliary variables to construct model-based small area estimators.These methods are applied to a redesign of the Dutch Crime Victimization Survey. [ABSTRACT FROM AUTHOR]