Judgmental Forecast


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Judgmental Forecast

A forecast made on subjective information. A judgmental forecast is made by a person thought to be knowledgeable about the company or market about which the forecast is being made. It may consider quantitative information, but it relies on a great deal of subjective feeling.
References in periodicals archive ?
The Greenbook forecast is a detailed judgmental forecast that until March 2010 (after which it became known as the Tealbook) was produced eight times a year by staff at the Board of Governors of the Federal Reserve System.
Econometric and judgmental forecasts were obtained from commercial forecasting agencies.
Research suggests that judgmental forecasts can also be improved by simply averaging the results of multiple independent forecasts.
Improving Forecasting Accuracy by Combining Statistical and Judgmental Forecasts in Tourism.
In a later work Sanders and Ritzman (2004), distinguishes between the marketing function, which more typically generates judgmental forecasts, and operations, which rely more heavily on quantitative data, and suggests integration techniques for these approaches.
In general, they found the judgmental forecasts to be less than optimal.
Future research related to the PMI should explore the use of combining judgmental forecasts with the quantitative forecasting of the PMI.
The main claims Sims makes are, first, that the Federal Reserve forecasts well, especially when forecasting inflation; second, that the informational contents of different forecasts are highly correlated, so that strong claims of superiority of one forecast over another should be treated as suspect; and third, that there does not appear to be strong evidence that the judgmental forecasts of the Federal Reserve are superior (as measured by the root mean square forecast error) to its model-based forecasts.
One difficulty with judgmental forecasts, however, is that it is hard, if not impossible, for an outside observer to trace the source of systematic forecast errors because there is no formal model of how the data were used.
Much of this work has concentrated on forecasts produced by various time series methods of extrapolation for individual series, although there have also been other studies comparing econometric and/or judgmental forecasts with the consensus.
The advantages and limitations of economic models and judgmental forecasts are reviewed, and a process that incorporates features of both is recommended.
Averaging can also work with purely judgmental forecasts. The error associated with these forecasts, much less averages of them, have not been studied in nearly as much depth as quantitative forecasts, so the guidance on how many forecasts to average is not as definitive.