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Forecasting and Uncertainty: A Survey

dc.contributor.authorMakridakis, Spyros
dc.contributor.authorBakas, Nikolas
dc.date.accessioned2016-01-20T13:54:38Z
dc.date.available2016-01-20T13:54:38Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/11728/7084
dc.description.abstractThe origins of forecasting can be traced back to the beginning of human civilization with attempts to predict the weather, although forecasting as a field first appeared in the 1940s and attracted more followers from the early 1950s, when the need for predictions emerged in different fields of endeavor. It expanded considerably in the 1960s and 1970s when benefits were ascertained and computers were employed to perform the tedious calculations required. But initial successes in the fields of economics and business were first moderated and later reversed, with reality checks, first during the 1973/74 energy crisis, afterwards during the prolonged economic stagflation of the late 1970s and early 1980s and further deteriorated during the severe 2007/8 global financial crisis. The initial, optimistic expectations that social sciences will (using powerful computers and sophisticated models) replicate the predictive accuracy of hard ones were repeatedly shattered. This has left diverse fields like economics, management, political and human sciences and even worse medicine with no objective evidence of successful, accurate predictions, casting doubts to their “scientific” vigor. At the same time, weather forecasting achieved success for immediate term predictions improving its accuracy and reliability over time. This paper starts with a historical overview of non-superstition based forecasting as it is practiced in different areas and surveys their predictive accuracy, highlighting their successes, identifying their failures and explaining the reasons involved. Consequently, it argues for a new, pragmatic approach where the emphasis must shift from forecasting to assessing uncertainty, as realistically as possible, evaluating its implications to risk and exploring ways to prepare to face it. It expands Rumsfeld’s classification to four quadrants (Known/Knowns, Unknown/Knowns, Known/Unknowns and Unknown/Unknowns) in order to explore the full range of predictions and associated uncertainties and consider the implications and risks involved. Finally, there is a concluding section summarizing the findings and providing some suggestions for future research aimed at turning forecasting into an interdisciplinary field increasing its value and usefulness.en_UK
dc.language.isoenen_UK
dc.relation.ispartofseriesFORTHCOMING: Risk and Decision Analysis Journal;
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_UK
dc.subjectForecastingen_UK
dc.subjectUncertaintyen_UK
dc.subjectRisken_UK
dc.subjectTime Seriesen_UK
dc.subjectEconometricsen_UK
dc.subjectAveragingen_UK
dc.subjectDecision Rulesen_UK
dc.subjectJudgmental Forecastsen_UK
dc.subjectPrediction Marketsen_UK
dc.subjectSimple versus Sophisticated Modelsen_UK
dc.titleForecasting and Uncertainty: A Surveyen_UK
dc.typeArticleen_UK


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