Measuring uncertainties: a theoretical approach. (2019)

• Record Type: Journal Article
• Authors: Facioni, Carolina; Corazziari, Isabella; Maggino, Filomena
• Is Part Of: International journal of computational economics and econometrics. Volume 9:Number 1/2(2019)
• Page Start: 5
• Page End: 28
• Publication Date: 2019
• Subjects: uncertainty measure -- futures studies -- DFA -- dynamic factor analysis; Econometrics -- Periodicals; Economics -- Data processing -- Periodicals; 330.01519505
• Languages: English
• ISSNs: 1757-1170

Abstract

When our aim is to draw the possible developments of future events, we are faced with a practical obstacle. Indeed, we cannot have any empirical experience of the future. Have we, therefore, to be inferred that forecasting, exploring future or, better: exploring futures, or anticipating futures have not to be considered activities of a scientific kind? Answer to such a difficult question requires a multidisciplinary approach, where statistical models, methodology of social science and of course statistics and sociology as a whole - are enhanced in their ability to express the change - and sometimes the risk that the change itself implies. A great help in understanding complexity, and trends, comes from a method for multi-way data, based on the joint application of a factorial analysis and regression over time, called dynamic factor analysis (DFA).