Periodic models for seasonal data allow the parameters of the model to vary across the different seasons. This paper uses the components of UK consumption to see whether the periodic autoregressive (PAR) model yields more accurate forecasts than non-periodic models, such as the airline model of Box and Jenkins (1970), and autoregressive models that pre-test for (seasonal) unit roots. We analyse possible explanations for the relatively poor forecast performance of the periodic models that we find, notwithstanding the apparent support such models receive from the data in-sample.