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"Nowcasting and forecasting GDP growth with machine-learning sentiment indicators"

Author

Listed:
  • Oscar Claveria

    (AQR–IREA, Department of Econometrics, Statistics and Applied Economics, University of Barcelona, Diagonal 690, 08034 Barcelona, Spain.)

  • Enric Monte

    (Department of Signal Theory and Communications, Polytechnic University of Catalunya (UPC).)

  • Salvador Torra

    (Riskcenter–IREA, Department of Econometrics, Statistics and Applied Economics, University of Barcelona (UB).)

Abstract

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

Suggested Citation

  • Oscar Claveria & Enric Monte & Salvador Torra, 2021. ""Nowcasting and forecasting GDP growth with machine-learning sentiment indicators"," IREA Working Papers 202103, University of Barcelona, Research Institute of Applied Economics, revised Feb 2021.
    • Handle: RePEc:ira:wpaper:202103
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    References listed on IDEAS

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    Keywords

    Forecasting; Economic growth; Business and consumer expectations; Symbolic regression; Evolutionary algorithms; Genetic programming. JEL classification: C51; C55; C63; C83; C93.;
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