用LSTM预测罗马尼亚季度GDP，比传统模型更准更灵活
Advancing GDP Forecasting: The Potential of Machine Learning Techniques in Economic Predictions
- 用LSTM捕捉经济数据非线性关系，无需预设线性假设
- 在1995-2023数据上，LSTM预测未来4期的误差更低
- 适合关注宏观经济预测与机器学习结合的研究者
准确的经济预测长期以来依赖于计量经济学模型，这些模型通常假设数据具有线性关系和稳定性。然而，全球经济的复杂性和非线性特征要求探索替代方法。机器学习方法在预测国内生产总值（GDP）方面展现出显著优势，因其能建模复杂的非线性交互而无需明确指定底层关系。本文研究了循环神经网络（特别是长短期记忆网络，LSTM）在GDP预测中的有效性，并与传统计量方法SARIMA进行对比。使用1995年至2023年罗马尼亚季度GDP数据集，构建LSTM模型预测未来4个周期。结果表明，机器学习模型在预测准确性和灵活性方面持续优于传统计量模型。
原文摘要 · Abstract (English)
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
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