A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

In this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents’ expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Ec...

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Bibliographic Details
Published inSocial indicators research Vol. 135; no. 1; pp. 1 - 14
Main Authors Claveria, Oscar, Monte, Enric, Torra, Salvador
Format Journal Article Publication
LanguageEnglish
Published Dordrecht Springer Science + Business Media 01.01.2018
Springer Netherlands
Springer Nature B.V
Subjects
Algorithms
Economia i organització d'empreses
Economic conditions
Economic growth
Economic indicators
Economic models
Economic Research
Evolutionary algorithms
GDP
Genetic programming
Genetics
Global economy
Gross Domestic Product
Human Geography
Indicadors econòmics
Indicadors socials
Informàtica
Microeconomics
Polls & surveys
Predictions
Programació
Public Health
Quality of Life Research
Social indicators
Social research
Social Sciences
Sociology
Survey-based indicators
Symbolic regression
Tendency surveys
Àrees temàtiques de la UPC
Symbolic regression
Economic indicators
Evolutionary algorithms
Survey-based indicators
Tendency surveys
Genetic programming
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Summary:In this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents’ expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming we estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. We use the evolution of GDP as a target. This set of empirically-generated indicators of economic growth, are used as building blocks to construct an economic indicator. We compare the proposed indicator to the Economic Climate Index, and we evaluate its predictive performance to track the evolution of the GDP in ten European economies. We find that in most countries the proposed indicator outperforms forecasts generated by a benchmark model.
ISSN:0303-8300
1573-0921
DOI:10.1007/s11205-016-1490-3