Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact

TL;DR

This paper evaluates deep learning models for multi-country GDP prediction, comparing their performance with linear regression and examining the impact of novel light intensity data on forecasting accuracy.

Contribution

It provides a comprehensive analysis of deep learning versus linear regression for multi-country GDP forecasting, including the effect of new light intensity data.

Findings

Linear regression outperforms deep learning when only GDP growth values are used.

Deep learning models can surpass linear regression when using selected economic indicators.

Light intensity data does not significantly enhance GDP prediction accuracy.

Abstract

GDP is a vital measure of a country's economic health, reflecting the total value of goods and services produced. Forecasting GDP growth is essential for economic planning, as it helps governments, businesses, and investors anticipate trends, make informed decisions, and promote stability and growth. While most previous works focus on the prediction of the GDP growth rate for a single country or by machine learning methods, in this paper we give a comprehensive study on the GDP growth forecasting in the multi-country scenario by deep learning algorithms. For the prediction of the GDP growth where only GDP growth values are used, linear regression is generally better than deep learning algorithms. However, for the regression and the prediction of the GDP growth with selected economic indicators, deep learning algorithms could be superior to linear regression. We also investigate the influence of the novel data -- the light intensity data on the prediction of the GDP growth, and numerical experiments indicate that they do not necessarily improve the prediction performance. Code is provided at https://github.com/Sariel2018/Multi-Country-GDP-Prediction.git.