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Do judgmental adjustments of statistical forecasts of demand improve accuracy? Companies certainly believe adjustment helps: a survey by Fildes and Goodwin estimated that 34% of statistical forecasts were subsequently judgmentally adjusted. Now a large empirical study by Fildes, Goodwin, Lawrence, and Nikolopoulos (2009) has shown that on average adjustments do help, but only when something important has happened, is planned, or is expected that has not been included in the statistical model.

While management adjustments can incorporate useful information to improve forecast accuracy, they are expensive and they may introduce bias. Because the managers' adjustments in the study were made in an unstructured manner, it seems likely that further improvements in the use of managerial judgments are possible.

There has been little research on how best to forecast the effectiveness of altenative strategies for implementing government policies. It seems reasonable to assume that better forecasts of the effects of policy implementation would lead to better better policies and better implementation of those policies. Nicolas Savio and Konstantinos Nikolopoulos propose in their working paper an approach that combines two evidence-based forecasting methods: strunctured analogies (Green and Armstrong 2007) and econometric modelling.

Some places are still available in this training course run by the Lancaster Centre for Forecasting at Lancaster University. For information, see the course brochure.