In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 2 ( 2022-2-8), p. e0263391-
Abstract:
This paper aims to explore several ways to construct a scientific and comprehensive early warning system (EWS) for local government debt risk in China. In order to achieve this goal, this paper studies the local government debt risk from multiple perspectives, i.e., individual risk, contagion risk, static risk and dynamic risk. Firstly, taking China’s 30 provinces over the period of 2010~ 2018 as a sample, this paper establishes early warning indicators for individual risk of local government debt, and uses the network model to establish early warning indicators for contagion risk of local government debt. Then, this paper applies the criteria importance though intercrieria correlation (CRITIC) method and coefficient of variation method to obtain the proxy variable Ⅰ, which combines the above two risks. Secondly, based on the proxy variable Ⅰ, both the Markov-switching autoregressive (MS-AR) model and coefficient of variation method are used to obtain the proxy variable Ⅱ, which comprehensively considers the individual risk, contagion risk, static risk and dynamic risk of local government debt. Finally, machine learning algorithms are adopted to generalize the EWS designed in this paper. The results show that: (1) From different perspectives of local government debt risk, the list of provinces that require early warning is different; (2) The support vector machines can well generalize our EWS.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0263391
DOI:
10.1371/journal.pone.0263391.g001
DOI:
10.1371/journal.pone.0263391.g002
DOI:
10.1371/journal.pone.0263391.g003
DOI:
10.1371/journal.pone.0263391.g004
DOI:
10.1371/journal.pone.0263391.t001
DOI:
10.1371/journal.pone.0263391.t002
DOI:
10.1371/journal.pone.0263391.t003
DOI:
10.1371/journal.pone.0263391.t004
DOI:
10.1371/journal.pone.0263391.t005
DOI:
10.1371/journal.pone.0263391.t006
DOI:
10.1371/journal.pone.0263391.t007
DOI:
10.1371/journal.pone.0263391.t008
DOI:
10.1371/journal.pone.0263391.t009
DOI:
10.1371/journal.pone.0263391.t010
DOI:
10.1371/journal.pone.0263391.s001
DOI:
10.1371/journal.pone.0263391.s002
DOI:
10.1371/journal.pone.0263391.s003
DOI:
10.1371/journal.pone.0263391.s004
DOI:
10.1371/journal.pone.0263391.r001
DOI:
10.1371/journal.pone.0263391.r002
DOI:
10.1371/journal.pone.0263391.r003
DOI:
10.1371/journal.pone.0263391.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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