A new approach for the quantification of qualitative measures of economic expectations
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 abou...
| Authors: | , , |
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| Format: | article |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2016 |
| Country: | España |
| Institution: | Universidad de Barcelona |
| Repository: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/117334 |
| Online Access: | https://hdl.handle.net/2445/117334 |
| Access Level: | Open access |
| Keyword: | Creixement econòmic Investigació qualitativa Anàlisi de regressió Algorismes genètics Economic growth Qualitative research Regression analysis Genetic algorithms |
| Summary: | 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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