Construction and Empirical Analysis of Macroeconomic Forecasting Models Based on Machine Learning

Abstract

Macroeconomic forecasting is an important basis for policy-making and investment decision. However, traditional econometric models always show some limitations in handling non-linear and high-dimensional data environment, especially in the face of more complex and fluctuated global economic environment. The complexity gradually increases, and more adaptable and robust analytical tools are needed to capture the intricate patterns and relationships between economic variables. This paper constructs a forecasting model based on advanced machine learning methods combining time series features and macroeconomic index, and compares the LSTM neural network and Random Forest algorithm. Based on China’s macroeconomic data from 2010 to 2023, the empirical results show that machine learning models have obvious advantages over the traditional ARIMA model in forecasting two important indicators GDP growth rate and inflation rate, and the average forecasting error is reduced by 18.7%. The empirical results of machine learning models show that these models are better at capturing temporal dependencies and feature interactions than traditional models. This study not only provides new support for improving the accuracy of macroeconomic forecasting, but also verifies the strong application significance of machine learning in quantitative economics and proves that using these techniques can make economic policies and investment strategies more accurate and responsive in rapidly changing economies such as China.