What is it about?

We apply the two-step machine-learning method proposed by Claveria et al. (2021) to generate country-specific sentiment indicators that provide estimates of year-on-year GDP growth rates. In the first step, by means of genetic programming, business and consumer expectations are evolved to derive sentiment indicators for 19 European economies. In the second step, the sentiment indicators are iteratively re-computed and combined each period to forecast yearly growth rates. To assess the performance of the proposed approach, we have designed two out-of-sample experiments: a nowcasting exercise in which we recursively generate estimates of GDP at the end of each quarter using the latest survey data available, and an iterative forecasting exercise for different forecast horizons.

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Why is it important?

We find that forecasts generated with the sentiment indicators outperform those obtained with time series models. These results show the potential of the proposed methodology as a predictive tool.


The generated 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.

Oscar Claveria
AQR-IREA, Univeristy of Barcelona

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This page is a summary of: Nowcasting and forecasting GDP growth with machine-learning sentiment indicators, SSRN Electronic Journal, January 2021, Elsevier, DOI: 10.2139/ssrn.3787570.
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