Forecasting labour market flows is important for budgeting and decision-making in government departments and public administration. Macroeconomic forecasts are normally obtained from time series data. In this article, we follow another approach that uses individual-level statistical analysis to predict the number of exits out of unemployment insurance claims. We present a comparative study of econometric, actuarial and statistical methodologies that base on different data structures. The results with records of the German unemployment insurance suggest that prediction based on individual-level statistical duration analysis constitutes an interesting alternative to aggregate data-based forecasting. In particular, forecasts of up to six months ahead are surprisingly precise and are found to be more precise than considered time series forecasts.