| Abstract: |
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. |