Impact of corrections for dynamic selection bias on forecasts of retention behavior

We investigate the impact of corrections for dynamic selection bias on forecasting accuracy in a multi-period stay|leave model. While corrections for selection bias are needed for consistent coefficient estimates, they do not necessarily produce more accurate forecasts than uncorrected techniques. Theorem 1 shows that, apart from estimation errors, a shrinkage principle applies: the heterogeneity restriction imposed by uncorrected and combination techniques improves accuracy for forecasting individuals that leave, and hurts accuracy for forecasting individuals that stay. This has important implications for decision making because of the potential for asymmetric losses. We also present an illustrative empirical application and results from Monte Carlo experiments. We find that differences in relative accuracy vary directly with the degree of selection bias and inversely with the percentage of the initial population that stays. Copyright © 2007 John Wiley & Sons, Ltd.