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Gross domestic product (GDP) is the most widely used indicator inmacroeconomics and the main tool for measuring a country's economic ouput. Dueto the diversity and complexity of the world economy, a wide range of modelshave been used, but there are challenges in making decadal GDP forecasts givenunexpected changes such as pandemics and wars. Deep learning models are wellsuited for modeling temporal sequences have been applied for time seriesforecasting. In this paper, we develop a deep learning framework to forecastthe GDP growth rate of the world economy over a decade. We use Penn World Tableas 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 multipledeep learning models, LSTM, BD-LSTM, ED-LSTM and CNN, and compared theirresults with the traditional time series model (ARIMA,VAR). Our resultsindicate that ED-LSTM is the best performing model. We present a recursive deeplearning framework to predict the GDP growth rate in the next ten years. Wepredict that most countries will experience economic growth slowdown,stagnation or even recession within five years; only China, France and Indiaare predicted to experience stable, or increasing, GDP growth.

Recursive deep learning framework for forecasting the decadal world economic outlook