Journal article 13 views
How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning
Research in International Business and Finance, Volume: 74, Start page: 102728
Swansea University Author:
Mohammad Abedin
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1016/j.ribaf.2024.102728
Abstract
This paper employs the double machine learning model to investigate the impact of urban investment bonds on economic resilience. To deal with a broad set of macroeconomic and industry variables, LASSO is used for model estimation. The sample consists of 239 Chinese cities that issued debt and loan i...
| Published in: | Research in International Business and Finance |
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| ISSN: | 0275-5319 |
| Published: |
Elsevier BV
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68661 |
| Abstract: |
This paper employs the double machine learning model to investigate the impact of urban investment bonds on economic resilience. To deal with a broad set of macroeconomic and industry variables, LASSO is used for model estimation. The sample consists of 239 Chinese cities that issued debt and loan instruments between 2016 and 2021. The results show that 1) urban investment bonds have a positive, inverted U-shaped effect on economic resilience; 2) the ability to recover from an economic shock plays an important role in constructing the Chinese economic resilience index. The heterogeneity analysis reveals that the impact of urban investment bonds on economic resilience varies according to cities’ locations, industrial structure, and financial structure. Furthermore, the mechanism analysis demonstrates that urban investment bonds enhance economic resilience by promoting infrastructure development. These findings provide helpful guidance for China and other developing countries to ensure financing security and maintain robust economic growth. |
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| Keywords: |
Economic Resilience, Urban Investment Debts, Double Machine Learning, LASSO Technique, Heterogeneity Analysis |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
This paper was supported by the Major Program of the National Social Science Foundation of China (Grant No. 21BTJ047) |
| Start Page: |
102728 |