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.
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Acknowledgments
This paper has been partially financed by the project SpeechTech4All (TEC2012-38939-C03-02). We would like to thank the Editor and an anonymous referee for their useful comments and suggestions. This paper has been partially supported by the Spanish Ministry of Economy and Competitiveness (TEC2015-69266-P). We also wish to thank Johanna Garnitz and Klaus Wohlrabe at the Ifo Institute for Economic Research in Munich for providing us the data used in the study.
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Claveria, O., Monte, E. & Torra, S. A new approach for the quantification of qualitative measures of economic expectations. Qual Quant 51, 2685–2706 (2017). https://doi.org/10.1007/s11135-016-0416-0
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DOI: https://doi.org/10.1007/s11135-016-0416-0
Keywords
- Economic growth
- Qualitative survey data
- Expectations
- Symbolic regression
- Evolutionary algorithms
- Genetic programming