Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/246273 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Applied Artificial Intelligence [ISSN:] 1087-6545 [Issue:] forthcoming [Publisher:] Taylor & Francis [Place:] London [Year:] 2021
Verlag: 
Taylor & Francis, London
Zusammenfassung: 
This study uses machine learning techniques to identify the key drivers of financial development in Africa. To this end, four regularization techniques— the Standard lasso, Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and the Elasticnet are trained based on a dataset containing 86 covariates of financial development for the period 1990 – 2019. The results show that variables such as cell phones, economic globalisation, institutional effectiveness, and literacy are crucial for financial sector development in Africa. Evidence from the Partialing-out lasso instrumental variable regression reveals that while inflation and agricultural sector employment suppress financial sector development, cell phones and institutional effectiveness are remarkable in spurring financial sector development in Africa. Policy recommendations are provided in line with the rise in globalisation, and technological progress in Africa.
Schlagwörter: 
Africa
Elasticnet
Financial Development
Financial Inclusion
Lasso
Regularization
Variable Selection
JEL: 
C01
C14
C52
C53
C55
E5
O55
DOI der veröffentlichten Version: 
Dokumentart: 
Article
Dokumentversion: 
Manuscript Version (Preprint)
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