Gross domestic product (GDP) is the most widely used indicator in macroeconomics and the main tool for measuring a country's economic ouput. Due to the diversity and complexity of the world economy, a wide range of models have been used, but there are challenges in making decadal GDP forecasts given unexpected changes such as pandemics and wars. Deep learning models are well suited for modeling temporal sequences have been applied for time series forecasting. In this paper, we develop a deep learning framework to forecast the GDP growth rate of the world economy over a decade. We use Penn World Table as the source of our data, taking data from 1980 to 2019, across 13 countries, such as Australia, China, India, the United States and so on. We test multiple deep learning models, LSTM, BD-LSTM, ED-LSTM and CNN, and compared their results with the traditional time series model (ARIMA,VAR). Our results indicate that ED-LSTM is the best performing model. We present a recursive deep learning framework to predict the GDP growth rate in the next ten years. We predict that most countries will experience economic growth slowdown, stagnation or even recession within five years; only China, France and India are predicted to experience stable, or increasing, GDP growth.

翻译：国内生产总值(GDP)是宏观经济学中最广泛使用的指标,也是衡量国家经济产出的主要工具。鉴于世界经济的多样性与复杂性,已有多种模型被应用,但在面对疫情和战争等突发变化时,做出十年期GDP预测仍面临挑战。深度学习模型因擅长时间序列建模而适用于时间序列预测。本文构建了一种深度学习框架,用于预测未来十年世界经济的GDP增长率。我们以Penn World Table为数据源,选取1980年至2019年间涵盖澳大利亚、中国、印度、美国等13个国家的数据。我们测试了多种深度学习模型——LSTM、BD-LSTM、ED-LSTM和CNN,并将其结果与传统时间序列模型(ARIMA、VAR)进行对比。结果表明ED-LSTM性能最优。我们提出一个递归深度学习框架,用于预测未来十年的GDP增长率。预测结果显示,大多数国家将在五年内经历经济增长放缓、停滞甚至衰退;仅中国、法国和印度预计保持稳定或增长的GDP增速。