This paper applies causal machine learning methods to analyze the heterogeneous regional impacts of monetary policy in China. The method uncovers the heterogeneous regional impacts of different monetary policy stances on the provincial figures for real GDP growth CPI inflation and loan growth compared to the national averages. The varying effects of expansionary and contractionary monetary policy phases on Chinese provinces are highlighted and explained. Subsequently applying interpretable machine learning the empirical results show that the credit channel is the main channel affecting the regional impacts of monetary policy. An imminent conclusion of the uneven provincial responses to the "one size fits all" monetary policy is that different policymakers should coordinate their efforts to search for the optimal fiscal and monetary policy mix.
China ; monetary policy ; regional heterogeneity ; machine learning ; shadow banking (search for similar items in EconPapers)
No 62 WiSo-HH Working Paper Series from University of Hamburg Faculty of Business Economics and Social Sciences WISO Research Laboratory