If a balanced stabilization policy is to be conducted, a well updated basis for decision-making is required in the form of current assessments and forecasts. This is particularly important when major shocks hit the economy and stabilization policy is expected to play a greater role than normal. However, a large proportion of the models used by policy makers today do not meet this requirement as they are based on data observed at low frequencies, often quarterly. This project therefore aims to analyze whether "mixed frequency" models, i.e. models that use high frequency information to model low frequency series such as GDP, can contribute to better macroeconomic situation assessments and forecasts.