Quantitative policy analysts are usually confronted with the problem to derive a base-line scenario that reflects the most likely state of an economy in a future year. The methods used in practice to derive such a base-line scenarios are heterogeneous and range from the usage of the last observable year to complete and consistent estimation procedures. In the case of general equilibrium (CGE) analyses, the Scenar2020 project (European Commission 2006a) is one example how projections of macro-economic indicators (exogenous drivers) are used to construct the base-line as a model scenario: Starting from a calibrated version, exogenous variables are modified until macro-economic projections are met. However, numerous projections refer to economic indicators which are endogenous variables within the CGE framework, such as gross domestic product (GDP), market prices, or produced quantities. To investigate methods that allow integrating projections for endogenous CGE variables is the main topic of this study. Our starting point is the work by Arndt et al (2002), where entropy-based (Golan et al 1996) techniques are employed for the estimation of behavioural parameters by fitting a CGE model to time series on endogenous variables. Following this concept, we investigate a method to fit a CGE´s parameters and endogenous variables to market- and macro-economic projections from major research institutes.