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

Oscar Claveria, Enric Monte and Salvador Torra
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Enric Monte: Polytechnic University of Catalunya
Salvador Torra: University of Barcelona

Journal of Applied Economics, 2017, vol. 20, 329-349

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.

Keywords: business and consumer surveys; forecasting; economic growth; symbolic regression; evolutionary algorithms; genetic programming : (search for similar items in EconPapers)
JEL-codes: C51 C55 C63 C83 C93 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (11)

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Journal Article: Using Survey Data to Forecast Real Activity with Evolutionary Algorithms. a Cross-Country Analysis (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:cem:jaecon:v:20:y:2017:n:2:p:329-349

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