This paper is devoted to the study of judgmental forecasting. It contains general data about this type of forecasting, cases of its application, examples of its most effective use, and its advantages and disadvantages. As an example of judgmental forecasting, the Delphi method is proposed, which involves identifying a consistent assessment of an expert group through an independent, anonymous survey of experts in several rounds, which involves reporting the previous round’s results to the experts. Resources such as the book Forecasting: Principles and practice by Hyndman and Athanasopoulos, and Sniezek’s article “A comparison of techniques for judgmental forecasting by groups with common information” are used to support the presented data.
What is Judgmental Forecasting
In cases of the extreme complexity of the problem, its novelty, insufficiency of available information, or the impossibility of mathematical formalization of the solution process, one has to turn to competent experts. The solution of problems by experts, their commenting, the formation of quantitative estimates, and the processing of the latter by formal methods is called the method of judgmental forecasting. Hyndman and Athanasopoulos (2018) assert that this method involves a set of logical procedures aimed at obtaining from experts the data necessary for the preparation and selection of rational decisions. In the theoretical aspect, the legitimacy of using judgmental forecasting is confirmed because methodologically correctly obtained expert judgments meet two criteria for the reliability of any new knowledge: accuracy and reproducibility of the result. Therefore, judgmental forecasting is created by experts based on their experience and assessments.
Judgmental forecasting helps to formalize the procedures for collecting, summarizing, and analyzing expert opinions to transform them into the most convenient form for making an informed decision. Moreover, this method of forecasting is continuously developed and improved. Currently, judgmental forecasting, in combination with other mathematical and statistical methods, can minimize the risk of making the wrong decision.
Where is Judgmental Forecasting used Most Effectively?
According to Hyndman and Athanasopoulos (2018), judgmental forecasting is applied in certain cases. Firstly, when an object or some phenomenon cannot be described mathematically. Secondly, it is used when there is no reliable, accurate, statistical information about the object that allows the use of objective methods. Thirdly, when there are no means for mathematical processing. For example, if there is no computer equipment, software, or qualified personnel. Fourth, it is a suitable method for an emergency situation when a quick decision is needed. Thus, there are four main cases in which judgmental forecasting is used.
Examples of how Judgmental Forecasting is Used Most Effectively
One example of the most effective use of judgmental forecasting may be the case of central banks. Hyndman and Athanasopoulos (2018) note that they involve judgment in forecasting the current level of economic activity, a procedure known as nowcasting because GDP data is only available quarterly. Moreover, this type of forecasting model is especially useful in such fields as research and development. Focus groups and expert groups can provide information that no computer model can provide. For example, by asking a group of people about what they are looking for in a product, companies can gauge their direction in developing specific product features.
Advantages and Disadvantages of Judgmental Forecasting
Like any other phenomenon, judgmental forecasting has both advantages and disadvantages.
Advantages
The undoubted advantage of judgmental forecasting is that it is universal and applicable to various forecasting objects. Moreover, it is relatively simple from a methodological point of view and does not place high demands on the quality of the initial basic information. Sniezek (1990) notes that the scope of statistical forecasting methods is limited to short- and medium-term forecasts. On the contrary, judgmental forecasting will allow to make long-term forecasts.
Disadvantages
Judgmental forecasting has several disadvantages. First, Hyndman and Athanasopoulos (2018) affirm that it can be inconsistent. Unlike statistical forecasting, which can be generated using the same mathematical formulas, judgmental forecasting relies heavily on human cognition and is subject to its limitations. For example, a limited attention span can lead to important information being missed, or a misunderstanding of cause and effect relationships can lead to erroneous conclusions. Moreover, human judgment may vary due to psychological factors. Second, judgment can be clouded by personal or political agendas when goals and projections are not separated. Even when goals and projections are separated, judgment can be clouded by optimism or wishful thinking. Third, there can be anchoring effects in judgmental forecasting where subsequent forecasts tend to converge or be close to the original familiar reference point. Anchoring can lead to conservatism and underestimation of new and more relevant information and thus create a systematic bias. Therefore, whenever possible, the judgmental results obtained by the methods of expert assessments should be supported by other forecasting methods.
The Delphi Method of Judgmental Forecasting
One of the methods for obtaining an individual opinion of experts is the Delphi method. Its essential features are anonymity, multi-level, and correspondence. According to Sniezek (1990), the Delphi method is effective because it allows to consider the opinions of all people who are relevant to any issue through a consistent combination of considerations, suggestions, and conclusions, and then come to a specific agreement. The advantage of the Delphi method is the use of feedback during the survey, which significantly increases the objectivity of expert assessments. Thus, using the Delphi method, expert opinions are determined by periodically weighing their views, taking into account the answers and arguments of colleagues.
References
Hyndman, R.J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OText.
Sniezek, J.A. (1990). A comparison of techniques for judgmental forecasting by groups with common information. Group & Organization Studies, 15(1), 5-19.