Nowcasting and Forecasting GDP Growth with Machine-Learning Sentiment Indicators

IREA Working Papers 2021/03. Research Institute of Applied Economics, University of Barcelona

AQR Working Papers 2021/01. Regional Quantitative Analysis Research Group, University of Barcelona

27 Pages Posted: 22 Mar 2021 Last revised: 3 Jun 2021

See all articles by Oscar Claveria

Oscar Claveria

University of Barcelona - Regional Quantitative Analysis Group (AQR-IREA)

Enric Monte

Polytechnic University of Catalunya

Salvador Torra

University of Barcelona - Riskcenter-IREA

Date Written: February 17, 2021

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.

Keywords: forecasting, economic growth, business and consumer expectations, symbolic regression, evolutionary algorithms, genetic programming

JEL Classification: C51, C55, C63, C83, C93

Suggested Citation

Claveria, Oscar and Monte, Enric and Torra, Salvador, Nowcasting and Forecasting GDP Growth with Machine-Learning Sentiment Indicators (February 17, 2021). IREA Working Papers 2021/03. Research Institute of Applied Economics, University of Barcelona, AQR Working Papers 2021/01. Regional Quantitative Analysis Research Group, University of Barcelona, Available at SSRN: https://ssrn.com/abstract=3787570 or http://dx.doi.org/10.2139/ssrn.3787570

Oscar Claveria (Contact Author)

University of Barcelona - Regional Quantitative Analysis Group (AQR-IREA) ( email )

Av. Diagonal 690
Barcelona, Barcelona 08034
Spain

HOME PAGE: http://www.ub.edu/aqr/fitxa-persones_en.php?id=8

Enric Monte

Polytechnic University of Catalunya ( email )

Universitat Polit├Ęcnica de Catalunya
Campus Nord, Jordi Girona 1-3
Barcelona, 08034
Spain

Salvador Torra

University of Barcelona - Riskcenter-IREA ( email )

Av. Diagonal, 690
Barcelona, 08034
Spain

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