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Especially in the insurance industry interest rate models play a crucial rolee.g. to calculate the insurance company's liabilities, performance scenarios orrisk measures. A prominant candidate is the 2-Additive-Factor Gaussian Model(Gauss2++) - in a different representation also known as the 2-FactorHull-White model. In this paper, we propose a framework to estimate the modelsuch that it can be applied under the risk neutral and the real world measurein a consistent manner. We first show that any progressive andsquare-integrable function can be used to specify the change of measure withoutloosing the analytic tractability of e.g. zero-coupon bond prices in bothworlds. We further propose two time dependent candidates, which are easy tocalibrate: a step and a linear function. They represent two variants of ourframework and distinguish between a short and a long term risk premium, whichallows to regularize the interest rates in the long horizon. We apply bothvariants to historical data and show that they indeed produce realistic andmuch more stable long term interest rate forecast than the usage of a constantfunction. This stability over time would translate to performance scenarios ofe.g. interest rate sensitive fonds and risk measures.

The Gauss2++ Model -- A Comparison of Different Measure Change Specifications for a Consistent Risk Neutral and Real World Calibration