In assessing the performance of population forecasts, it is useful to have a standard with which forecast errors can be compared. Univariate time series models may provide such a standard. Mean forecast errors of time series models indicate to what extent the movement of a variable could have been predicted from its own past. These errors show the degree of predictability that is attainable, at least in a given period. In this paper 3 time series methods (exponential smoothing, Box-Jenkins method, and structural time series models) are applied to Dutch data on births, deaths, marriages, immigrants, and emigrants. The variability of prediction errors between different periods is examined. The possibility that univariate predictions can be improved by using quarterly or monthly data instead of annual data is tested.