Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/260465 
Autor:innen: 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
WiSo-HH Working Paper Series No. 62
Verlag: 
Universität Hamburg, Fakultät für Wirtschafts- und Sozialwissenschaften, WiSo-Forschungslabor, Hamburg
Zusammenfassung: 
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.
Schlagwörter: 
China
monetary policy
regional heterogeneity
machine learning,shadow banking
JEL: 
E52
C54
R11
E61
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe
2.24 MB





Publikationen in EconStor sind urheberrechtlich geschützt.