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

Clavería, Oscar; Monte, Enric; Porras, Salvador Torra

doi:10.1007/s11205-016-1490-3

Cited by 14 publications

(7 citation statements)

References 84 publications

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“…Furthermore, we show the ability of GP to solve optimisation problems for economic analysis. This research links with previous works by Claveria et al (2018), Duda and Szydło (2011) and Kotanchek et al (2010), who made use of GP for the generation of empirical models used for the monitoring of the economic activity.…”

Section: Discussionsupporting

confidence: 67%

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Clavería

Monte

Porras

2021

Technological and Economic Development of Economy

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In this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for the non-linear combination of firms’ and households’ expectations that minimises a fitness function. Second, we compute the frequency with which each survey expectation appears in the evolved indicators and examine the lag structure per variable selected by the algorithm. The industry survey indicator with the highest predictive performance are production expectations, while in the case of the consumer survey the distribution between variables is multi-modal. Third, we evaluate the out-of-sample predictive performance of the generated indicators, obtaining more accurate estimates of year-on-year GDP growth rates than with the scaled industrial and consumer confidence indicators. Finally, we use non-linear constrained optimisation to combine the evolved expectations of firms and consumers and generate aggregate expectations of of year-on-year GDP growth. We find that, in most cases, aggregate expectations outperform recursive autoregressive predictions of economic growth.

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Section: Discussionsupporting

confidence: 67%

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Clavería

Monte

Porras

2021

Technological and Economic Development of Economy

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“…Marković et al (2017) made use of 10 science and technology factors as inputs for GDP growth prediction in 28 EU countries. More recently, Claveria et al (2018) applied a GP procedure to derive a set of building blocks used to estimate economic activity in the main European countries, finding that the proposed economic indicator outperformed a random walk model used as a benchmark in most cases.…”

Section: Applications Of Gp In Economic Researchmentioning

confidence: 99%

Unemployment expectations: A socio-demographic analysis of the effect of news

et al. 2019

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In this study, we evaluate the effect of news on consumer unemployment expectations for sixteen socio-demographic groups. To this end, we construct an unemployment sentiment indicator and extract news about several economic variables. By means of genetic programming we estimate symbolic regressions that link unemployment rates in the Euro Area to qualitative expectations about a wide range of economic variables. We then use the evolved expressions to compute unemployment expectations for each consumer group. We first assess the out-of-sample forecast accuracy of the evolved indicators, obtaining better forecasts for the leading unemployment sentiment indicator than for the coincident one. Results are similar across the different socio-demographic groups. The best forecast results are obtained for respondents between 30 and 49 years. The group where we observe the bigger differences among categories is the occupation, where the lowest forecast errors are obtained for the unemployed respondents. Next, we link news about inflation, industrial production, and stock markets to unemployment expectations. With this aim we match positive and negative news with consumers' unemployment sentiment using a distributed lag regression model for each news item. We find asymmetries in the responses of consumers' unemployment expectations to economic news: they tend to be stronger in the case of negative news, especially in the case of inflation.

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“…Eickmeier and Ng 2011 ; Bachmann and Sims 2012 ; Sorić 2018 ). With that in mind, it is no surprise that BCS data has recently been (mostly successfully) utilized for short-term forecasting of reference macroeconomic variables (Van Arle and Kappler 2012 ; Sorić et al 2013 ; Claveria et al 2018 ; 2019a ; 2020 ) and stock market returns (Akhtar et al 2010 ; Chen 2011 ), as well as for elucidating consumer expenditures and political attitudes (Nguyen and Claus 2013 ), etc. Despite the well-established usefulness of BCS in short-term macroeconomic predictions, quite a few empirical questions still remain unanswered.…”

Section: Introductionmentioning

confidence: 99%

Breaking new grounds: a fresh insight into the leading properties of business and consumer survey indicators

2022

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Ever since their initiation 60 years ago, the harmonized European Business and Consumer Surveys (BCS) have risen to the challenge of performing as a solid data pillar for quantifying leading indicators of economic activity. However, mainstream research mainly focuses on publicly available composite BCS confidence indicators and inspects their predictive accuracy. We depart from this stance by considering a battery of novel techniques for quantifying BCS-based leading indicators with the particular aim to evaluate their predictive characteristics compared to conventional BCS leading indicators. We build upon the recently established weighted balance method, forecast disagreement, and surprise index. Additionally, we differ from the standpoint of rational expectations by introducing indicators of irrational sentiment and adaptive expectations, which have not previously been used in BCS studies of this sort. Our analysis in industry, consumer, and retail trade sectors of 28 European economies reveals that most of these novel techniques (especially irrational sentiment and adaptive expectations) produce more accurate predictions of economic activity than standard BCS benchmarks. These results are robust to several panel estimation procedures (heterogeneous panel Granger causality test and panel vector autoregressions, in particular). Supplementary Information The online version contains supplementary material available at 10.1007/s11135-021-01306-4.

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

Cited by 14 publications

References 84 publications

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Unemployment expectations: A socio-demographic analysis of the effect of news

Breaking new grounds: a fresh insight into the leading properties of business and consumer survey indicators

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