On the basis of these considerations, we maintain four criteria for our model of dynamic interactions among marketing and performance variables. First, the model should provide a flexible treatment of both short- and long-term effects. Second, the model should be robust to deviations from stationary, particularly the presence of random walks in stock prices, which can lead to spurious regression problems. Third, the model should provide a forecast and expected baseline for each performance variable, so that we can capture the impact of unexpected events as deviations from the baseline. Both econometric models and survey methods have been shown to perform well in generating these. Consequently, our model uses forecasts based on an econometric model and controls for changes in analyst earnings expectations. Fourth, the model should allow for various dynamic feedback loops between marketing and business performance variables. In summary, the study of the longitudinal impact of new product introductions and promotional incentives requires a carefully designed system of equations that accounts both for the time-series properties of performance and marketing variables and for their dynamic interactions