Machine Learning for Economic Forecasting: An Application to China's GDP Growth
It addresses economic forecasting for policymakers and analysts, but is incremental as it applies existing methods to a specific dataset.
This paper applied machine learning models to forecast China's quarterly GDP growth, finding that they generally had lower average forecast errors than traditional econometric models or expert forecasts, especially during stable periods, though experts sometimes outperformed at inflection points.
This paper aims to explore the application of machine learning in forecasting Chinese macroeconomic variables. Specifically, it employs various machine learning models to predict the quarterly real GDP growth of China, and analyzes the factors contributing to the performance differences among these models. Our findings indicate that the average forecast errors of machine learning models are generally lower than those of traditional econometric models or expert forecasts, particularly in periods of economic stability. However, during certain inflection points, although machine learning models still outperform traditional econometric models, expert forecasts may exhibit greater accuracy in some instances due to experts' more comprehensive understanding of the macroeconomic environment and real-time economic variables. In addition to macroeconomic forecasting, this paper employs interpretable machine learning methods to identify the key attributive variables from different machine learning models, aiming to enhance the understanding and evaluation of their contributions to macroeconomic fluctuations.