Uncovering heterogeneous regional impacts of Chinese monetary policy

Andrew Tsang ()
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Andrew Tsang: ASEAN+3 Macroeconomic Research Office (AMRO)

Empirical Economics, 2024, vol. 67, issue 3, No 2, 915-940

Abstract: Abstract This paper applies causal machine learning methods to analyze the heterogeneous regional impacts of monetary policy in China. The methods uncover the heterogeneous monetary policy impacts 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.

Keywords: China; Monetary policy; Regional heterogeneity; Causal machine learning; Shadow banking (search for similar items in EconPapers)
JEL-codes: C54 E52 E61 R11 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00181-024-02575-2

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