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  1. Using machine learning to create a property tax roll
    evidence from the city of Kananga, DR Congo
    Author: Bergeron, Augustin; Fournier, Arnaud; Kabeya Kabeya, John; Tourek, Gabriel
    Published: November 2023
    Publisher:  The International Centre for Tax and Development at the Institute of Development Studies, Brighton, UK

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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    Nicht speichern
    Inter-library loan:
    No inter-library loan
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    Source: Union catalogues
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781804701539
    Other identifier:
    Series: ICTD working paper ; 176
    Subjects: property tax; machine learning; Democratic Republic of Congo; computer vision; property valuation; state capacity
    Scope: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  2. Demand estimation with text and image data
    Author: Compiani, Giovanni; Morozov, Ilya; Seiler, Stephan
    Published: October 2023
    Publisher:  CESifo, Munich, Germany

    We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual... more

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

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 63
    Inter-library loan:
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    We propose a demand estimation method that allows researchers to estimate substitution patterns from unstructured image and text data. We first employ a series of machine learning models to measure product similarity from products' images and textual descriptions. We then estimate a nested logit model with product-pair specific nesting parameters that depend on the image and text similarities between products. Our framework does not require collecting product attributes for each category and can capture product similarity along dimensions that are hard to account for with observed attributes. We apply our method to a dataset describing the behavior of Amazon shoppers across several categories and show that incorporating texts and images in demand estimation helps us recover a flexible cross-price elasticity matrix.

     
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/282383
    Series: CESifo working papers ; 10695 (2023)
    Subjects: demand estimation; unstructured data; computer vision; text models
    Scope: 1 Online-Ressource (circa 30 Seiten), Illustrationen