Tsang, Andrew (2021): Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy.
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Abstract
This paper applies causal machine learning methods to analyze the heterogeneous regional impacts of monetary policy in China. The method uncovers the heterogeneous regional im-pacts 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.
Item Type: | MPRA Paper |
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Original Title: | Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy |
English Title: | Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy |
Language: | English |
Keywords: | China, monetary policy, regional heterogeneity, machine learning, shadow banking |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C54 - Quantitative Policy Modeling C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes |
Item ID: | 110703 |
Depositing User: | Andrew Tsang |
Date Deposited: | 23 Nov 2021 09:24 |
Last Modified: | 23 Nov 2021 09:24 |
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URI: | https://mpra.ub.uni-muenchen.de/id/eprint/110703 |