A new approach for the quantification of qualitative measures of economic expectations
Oscar Claveria (),
Enric Monte () and
Salvador Torra ()
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Enric Monte: Polytechnic University of Catalunya (UPC)
Salvador Torra: University of Barcelona (UB)
Quality & Quantity: International Journal of Methodology, 2017, vol. 51, issue 6, 2685-2706
Abstract In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents’ expectations. The research focuses on experts’ expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents’ expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents’ judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance.
Keywords: Economic growth; Qualitative survey data; Expectations; Symbolic regression; Evolutionary algorithms; Genetic programming (search for similar items in EconPapers)
JEL-codes: C51 C55 C63 C83 C93 (search for similar items in EconPapers)
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