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

Abstract

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.

Improving the search ability of GP using semantic diversity and local search

“…It has been applied in various challenging problems successfully, e.g. assembly code generation, designing circuits, symbolic regression, scheduling, model discovery, etc [1,6,7,30]. As traditional GP acts on the syntactic level, a small syntactic modification can Jiayu Liang, Yaxin Lu, Han Wu Tianjin Key Laboratory of Autonomous Intelligent Technology and System Tiangong University, Tianjin 300387, China E-mail: liangjiayu@tiangong.edu.cn lead to a significant change in the solution fitness, which can lower the search efficiency [23].…”