Daily Archives: February 1, 2017

Judgmental Forecasting

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Human judgment permeates forecasting processes. In economic forecasting, judgment may be used in identifying the endogenous and exogenous variables, building structural equations, correcting for omitted variables, specifying expectations for economic indicators, and adjusting the model predictions in light of new information, official announcements, or “street” talk. Forecasters appear to be highly satisfied with judgmental approaches, preferring them over quantitative techniques due to reasons of accuracy and difficulties in obtaining the necessary data for quantitative approaches.

Judgmental biases argued to be especially relevant to forecasting include: illusory correlations, hindsight, selective perception, attribution of success and failure, underestimating uncertainty, optimism, overconfidence, and inconsistency in judgment. These biases could also be related to the organisational incentive system. For instance, forecasters mostly prefer to underforecast, justifying this tendency typically by their motivation to look better if the stated goals are surpassed, or by the choice to be conservative.

Judgmental forecast on the other hand, benefit from human ability to evaluate information that is difficult to quantify, as well as to accommodate changing constraints and dynamic environments. Extensive implications of judgmental forecasting performance necessitates detailed analysis targeted at educating the users and providers of forecasters to recognize those elements of the task which are best delegated to a statistical model and to focus their attention on the elements where their judgment is most valuable.