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Displaying results 1 to 4 of 4.

  1. State institutions and tax capacity
    an empirical investigation of causality
    Author: Akanbi, Olusegun A.
    Published: 2019
    Publisher:  International Monetary Fund, [Washington, DC]

    Would better state institutions increase tax collection, or would higher tax collection help improve state institutions? In the absence of conclusive guidance from theory, this paper searches for an empirical answer to this question, using a panel... more

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    Duale Hochschule Baden-Württemberg Heidenheim, Bibliothek
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    Universitätsbibliothek Leipzig
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    Leuphana Universität Lüneburg, Medien- und Informationszentrum, Universitätsbibliothek
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    Duale Hochschule Baden-Württemberg Mosbach, Bibliothek
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    E-Book Nationallizenz IMF
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    Hochschule Offenburg, University of Applied Sciences, Bibliothek Campus Offenburg
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    Hochschulbibliothek Pforzheim, Bereichsbibliothek Technik und Wirtschaft
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    Duale Hochschule Baden-Württemberg Villingen-Schwenningen, Bibliothek
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    Would better state institutions increase tax collection, or would higher tax collection help improve state institutions? In the absence of conclusive guidance from theory, this paper searches for an empirical answer to this question, using a panel dataset covering 110 non-resource-rich countries from 1996 to 2017. Employing a panel vector error correction model, the paper finds that tax capacity and state institutions cause and reinforce each other for a wide range of country groups. The bi-directional causality results suggest that developing tax capacity and building state institutions need to go hand in hand for best results, particularly in developing countries. Based on the impulse response analyses, the paper also finds that the causal effects in advanced economies are generally low in both directions, while in developing countries, both tax capacity and institutions shocks have larger positive impacts on institutions and tax capacity, respectively

     
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  2. Machine learning and causality
    the impact of financial crises on growth
    Author: Tiffin, Andrew
    Published: 2019
    Publisher:  International Monetary Fund, [Washington, DC]

    Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of... more

    Access:
    Verlag (kostenfrei)
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    Verlag (kostenfrei)

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    Orient-Institut Beirut
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    Online
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    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Potsdamer Straße
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    Universitätsbibliothek Braunschweig
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    Staats- und Universitätsbibliothek Bremen
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    Universitätsbibliothek Erfurt / Forschungsbibliothek Gotha, Universitätsbibliothek Erfurt
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    Bibliothek der Pädagogischen Hochschule Freiburg/Breisgau
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    Niedersächsische Staats- und Universitätsbibliothek Göttingen
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    Universitäts- und Landesbibliothek Sachsen-Anhalt / Zentrale
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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Technische Universität Hamburg, Universitätsbibliothek
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    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
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    Location:
    Duale Hochschule Baden-Württemberg Heidenheim, Bibliothek
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    e-Book Nationallizenz
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    Thüringer Universitäts- und Landesbibliothek
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    Fachhochschule Kiel, Zentralbibliothek
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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Universitätsbibliothek Leipzig
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    Leuphana Universität Lüneburg, Medien- und Informationszentrum, Universitätsbibliothek
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    Duale Hochschule Baden-Württemberg Mosbach, Bibliothek
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    E-Book Nationallizenz IMF
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    Hochschule Offenburg, University of Applied Sciences, Bibliothek Campus Offenburg
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    E-Book International Monetary Fund
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    Hochschulbibliothek Pforzheim, Bereichsbibliothek Technik und Wirtschaft
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    e-Book International Monetary Fund eLibrary
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    No loan of volumes, only paper copies will be sent

    Location:
    Hochschule Albstadt-Sigmaringen, Bibliothek Sigmaringen
    Inter-library loan:
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    Location:
    Duale Hochschule Baden-Württemberg Villingen-Schwenningen, Bibliothek
    Signature:
    E_Book IMF
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    Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example-assessing the impact of a hypothetical banking crisis on a country's growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond

     
    Export to reference management software   RIS file
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  3. State institutions and tax capacity
    an empirical investigation of causality
    Author: Akanbi, Olusegun A.
    Published: 2019
    Publisher:  International Monetary Fund, [Washington, DC]

    Would better state institutions increase tax collection, or would higher tax collection help improve state institutions? In the absence of conclusive guidance from theory, this paper searches for an empirical answer to this question, using a panel... more

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

    Location:
    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Unter den Linden
    Inter-library loan:
    Unlimited inter-library loan, copies and loan

     

    Would better state institutions increase tax collection, or would higher tax collection help improve state institutions? In the absence of conclusive guidance from theory, this paper searches for an empirical answer to this question, using a panel dataset covering 110 non-resource-rich countries from 1996 to 2017. Employing a panel vector error correction model, the paper finds that tax capacity and state institutions cause and reinforce each other for a wide range of country groups. The bi-directional causality results suggest that developing tax capacity and building state institutions need to go hand in hand for best results, particularly in developing countries. Based on the impulse response analyses, the paper also finds that the causal effects in advanced economies are generally low in both directions, while in developing countries, both tax capacity and institutions shocks have larger positive impacts on institutions and tax capacity, respectively

     
    Export to reference management software   RIS file
      BibTeX file
  4. Machine learning and causality
    the impact of financial crises on growth
    Author: Tiffin, Andrew
    Published: 2019
    Publisher:  International Monetary Fund, [Washington, DC]

    Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of... more

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

    Location:
    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Unter den Linden
    Inter-library loan:
    Unlimited inter-library loan, copies and loan

     

    Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example-assessing the impact of a hypothetical banking crisis on a country's growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond

     
    Export to reference management software   RIS file
      BibTeX file