Among explanations below, which one is not a reason to favor a probability model over a regression

| August 30, 2017

Question
Among explanations below, which one is not a reason to favor a probability model over a regression like model for long-run projections of customer behavior?

A. In a regression-like model, it is necessary (and potentially difficult) to project future values for the independent variables.

B. Regression like models are fine for a one-period-ahead prediction, but not beyond that horizon.

C. If the observed behavior is viewed in an as if random manner, it would be wrong to put it into a regression like model as if its deterministically true.

D. Regression like models can’t capture non stationarity (i.e. changes in behavioral propensities over time.

E. Its often hard to come up

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