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Social Indicators Research

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09.11.2016

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

verfasst von: Oscar Claveria, Enric Monte, Salvador Torra

Erschienen in: Social Indicators Research | Ausgabe 1/2018

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Abstract

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.

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Titel: A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms
verfasst von: Oscar Claveria
Enric Monte
Salvador Torra
Publikationsdatum: 09.11.2016
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 1/2018
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-016-1490-3

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