9 - Predictability

Summary

This chapter opens with some quotes and insights on megaprojects. We turn to the construction and the use of prediction intervals in a time series context. We see that depending on the choice of the number of unit roots (stochastic trends) or the sample size (when does the sample start?), we can compute a wide range of prediction intervals. Next, we see that those trends, and breaks in levels and breaks in trend, can yield a wide variety of forecasts. Again, we reiterate that maintaining a variety of models and outcomes is useful, and that an equal-weighted combination of results can be most appropriate. Indeed, any specific choice leads to a different outcome. Finally, we discuss for a simple first-order autoregression how you can see what the limits to predictability are. We see that these limits are closer than we may think at the onset.