MULTILEVEL TIME-SERIES MODELS WITH APPLICATIONS TO REPEATED-MEASURES DATA

The analysis of repeated measures data can be conducted efficiently us ing a two-level random coefficients model. A standard assumption is th at the within-individual (level 1) residuals are uncorrelated. In some cases, especially where measurements are made close together in time, this may not be reasonable and this additional correlation structure should also be modelled. A time series model for such data is proposed which consists of a standard multilevel model for repeated measures d ata augmented by an autocorrelation model for the level 1 residuals. F irst- and second-order autoregressive models are considered in detail, together with a seasonal component. Both discrete and continuous time are considered and it is shown how the autocorrelation parameters can themselves be structured in terms of further explanatory variables. T he models are fitted to a data set consisting of repeated height measu rements on children.