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Article

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Accepted version

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2017-11

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cc-by-nc-nd (c) Elsevier, 2017
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/117331

Using survey data to forecast real activity with evolutionary algorithms. A cross-country analysis

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https://doi.org/10.1016/S1514-0326(17)30015-6

Abstract

In this study we use survey expectations about a wide range of economic variables to forecast real activity. We propose an empirical approach to derive mathematical functional forms that link survey expectations to economic growth. Combining symbolic regression with genetic programming we generate two survey-based indicators: a perceptions index, using agents' assessments about the present, and an expectations index with their expectations about the future. In order to find the optimal combination of both indexes that best replicates the evolution of economic activity in each country we use a portfolio management procedure known as index tracking. By means of a generalized reduced gradient algorithm we derive the relative weights of both indexes. In most economies, the survey-based predictions generated with the composite indicator outperform the benchmark model for one-quarter ahead forecasts, although these improvements are only significant in Austria, Belgium and Portugal.

Subject

Enquestes de consum, Creixement econòmic, Anàlisi de regressió, Algorismes genètics

Subject (English)

Consumer surveys, Economic growth, Regression analysis, Genetic algorithms

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Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

Citation

CLAVERÍA GONZÁLEZ, Óscar, MONTE MORENO, Enric and TORRA PORRAS, Salvador. Using survey data to forecast real activity with evolutionary algorithms. A cross-country analysis. Journal Of Applied Economics. 2017. Vol. 20, num. 2, pags. 329-349. ISSN 1514-0326. [consulted: 7 of August of 2026]. Available at: https://hdl.handle.net/2445/117331

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