Advancing GDP Forecasting: The Potential of Machine Learning Techniques in Economic Predictions

TL;DR

This paper explores the use of LSTM neural networks for GDP forecasting, demonstrating that machine learning models outperform traditional econometric methods like SARIMA in accuracy and flexibility.

Contribution

It introduces the application of LSTM networks to GDP forecasting and compares their performance against traditional econometric models using real-world data.

Findings

LSTM models outperform SARIMA in predictive accuracy.

Machine learning models handle nonlinear economic patterns better.

LSTM provides more flexible and accurate GDP forecasts.

Abstract

The quest for accurate economic forecasting has traditionally been dominated by econometric models, which most of the times rely on the assumptions of linear relationships and stationarity in of the data. However, the complex and often nonlinear nature of global economies necessitates the exploration of alternative approaches. Machine learning methods offer promising advantages over traditional econometric techniques for Gross Domestic Product forecasting, given their ability to model complex, nonlinear interactions and patterns without the need for explicit specification of the underlying relationships. This paper investigates the efficacy of Recurrent Neural Networks, in forecasting GDP, specifically LSTM networks. These models are compared against a traditional econometric method, SARIMA. We employ the quarterly Romanian GDP dataset from 1995 to 2023 and build a LSTM network to forecast to next 4 values in the series. Our findings suggest that machine learning models, consistently outperform traditional econometric models in terms of predictive accuracy and flexibility