Direkt zur Hauptnavigation springen Direkt zum Inhalt springen
info(at)avldigital.remove-this.de
Deutsch English
Letzte Suchanfragen

Ergebnisse für *

Zeige Ergebnisse 1 bis 2 von 2.

  1. What drives financial sector development in Africa?
    insights from machine learning
    Autor*in: Ofori, Isaac Kwesi; Quaidoo, Christopher; Ofori, Pamela E.
    Erschienen: [2021]
    Verlag:  African Governance and Development Institute, [Yaoundé]

    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... mehr

    Zugang:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 524
    Fernleihe:
    keine Fernleihe

     

    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.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/249082
    Schriftenreihe: AGDI working paper ; WP/21, 074
    Schlagworte: Africa; Elasticnet; Financial Development; Financial Inclusion; Lasso; Regularization; Variable Selection
    Umfang: 1 Online-Ressource (circa 37 Seiten), Illustrationen
  2. What really drives economic growth in sub-Saharan Africa?
    evidence from the lasso regularization and inferential techniques
    Autor*in: Ofori, Isaac Kwesi; Obeng, Camara Kwasi; Asongu, Simplice
    Erschienen: [2022]
    Verlag:  African Governance and Development Institute, [Yaoundé]

    The question of what really drives economic growth in sub-Saharan Africa (SSA) has been debated for many decades now. However, there is still a lack of clarity on variables crucial for driving growth as prior contributions have been executed at the... mehr

    Zugang:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 524
    Fernleihe:
    keine Fernleihe

     

    The question of what really drives economic growth in sub-Saharan Africa (SSA) has been debated for many decades now. However, there is still a lack of clarity on variables crucial for driving growth as prior contributions have been executed at the backdrop of preferential selection of covariates in the midst several of potential drivers of economic growth. The main challenge with such contribution is that even tenuous variables may be deemed influential under some model specifications and assumptions. To address this and inform policy appropriately, we train algorithms for four machine learning regularization techniques- the Standard lasso, the Adaptive lasso, the Minimum Schwarz Bayesian information criterion lasso, and the Elasticnet to study patterns in a dataset containing 113 covariates and identify the key variables affecting growth in SSA. We find that only 7 covariates are key for driving growth in SSA. Estimates of these variables are provided by running the lasso inferential techniques of double-selection linear regression, partialing-out lasso linear regression, and partialing-out lasso instrumental variable regression. Policy recommendations are also provided in line with the AfCFTA and the green growth agenda of the region.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/269068
    Schriftenreihe: AGDI working paper ; WP/22, 061
    Schlagworte: Economic growth; Elasticnet; Lasso; Machine learning; Partialing-out IV regression; sub-Saharan Africa
    Umfang: 1 Online-Ressource (circa 39 Seiten), Illustrationen