Reassessment of Factors Affecting China’s Quantity-Based Monetary Policy Effectiveness: An Interpretable Machine Learning Approach

Effective transmission of monetary policy serves as the foundational institutional guarantee for sustaining macroeconomic stability, smoothing cyclical economic fluctuations, and promoting high-quality development. Nevertheless, the underlying determinants driving the time-varying effectiveness of China’s quantity-based monetary policy have not been systematically and empirically delineated in the prevailing literature. This paper first constructs a precise measurement indicator for the effectiveness of China’s quantity-based monetary policy from the output-transmission dimension, which is defined as the response of domestic real output (excluding the contribution of net exports) to orthogonalized exogenous M2 growth shocks. On this basis, the gradient-boosting decision tree (GBDT) model is integrated with the Shapley Additive Explanations (SHAP) framework to quantitatively identify the core determinants that govern the policy effectiveness across different economic cycles and structural transformation stages. The estimation results document clear stage-wise heterogeneity in the drivers of China’s quantity-based monetary policy effectiveness: population-aging and macroeconomic-policy indicators stand out as the dominant explanatory factors over 2002–2008, while economic-structure indicators assume the leading role in shaping policy effectiveness during 2009–2015. The 2016–2022 period is further characterized by the joint dominance of demographic aging and economic-structure dimensions. Within this latest phase, the old-age dependency ratio, real–virtual economy structural misalignment, and distorted aggregate supply configuration exert statistically significant negative marginal contributions to the model-predicted effectiveness of monetary policy, whereas the total fertility rate and potential output growth rate yield positive and economically meaningful contributions.