Machine-learning sentiment indicators for European countries

What is it about?

We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve the business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a nowcasting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes.

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Photo by Sangharsh Lohakare on Unsplash

Photo by Sangharsh Lohakare on Unsplash

Why is it important?

The presented machine-learning sentiment indicators are easy to implement in order to nowcast GDP year-on-year growth rates. The proposed data-driven approach helps to monitor the evolution of the economy, both from the demand and supply sides.

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