  Forecasting with longitudinal data has been rarely studied. Most of the
available studies are for continuous response and all of them are for
univariate response. In this study, we consider forecasting multivariate
longitudinal binary data. Five different models including simple ones,
univariate and multivariate marginal models, and complex ones, marginally
specified models, are studied to forecast such data. Model forecasting
abilities are illustrated via a real life data set and a simulation study. The
simulation study includes a model independent data generation to provide a fair
environment for model competitions. Independent variables are forecast as well
as the dependent ones to mimic the real life cases best. Several accuracy
measures are considered to compare model forecasting abilities. Results show
that complex models yield better forecasts.
