Skip to main navigation Skip to main content
info(at)avldigital.remove-this.de
Deutsch English
Narrow Search
Search narrowed by
Last searches

Results for *

Displaying results 1 to 3 of 3.

  1. Estimation of preference heterogeneity in markets with costly search
    Author: Dong, Xiaojing; Morozov, Ilya; Seiler, Stephan; Hou, Liwen
    Published: [2018]
    Publisher:  [Stanford Graduate School of Business], [Stanford, CA]

    We study the estimation of preference heterogeneity in markets where consumers engage in costly search to learn product characteristics. Costly search amplifies the way consumer preferences translate into purchase probabilities, generating a... more

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

    Location:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Inter-library loan:
    No inter-library loan

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Inter-library loan:
    No inter-library loan

     

    We study the estimation of preference heterogeneity in markets where consumers engage in costly search to learn product characteristics. Costly search amplifies the way consumer preferences translate into purchase probabilities, generating a seemingly large degree of preference heterogeneity. We develop a search model that allows for flexible preference heterogeneity and estimate its parameters using a unique panel dataset on the search and purchase behavior of consumers. The results reveal that when search costs are ignored, the model overestimates standard deviations of product intercepts by 53%. We show that the bias in heterogeneity estimates leads to incorrect inference about price elasticities and seller markups and has important consequences for personalized pricing

     
    Export to reference management software   RIS file
      BibTeX file
    Content information
    Volltext (kostenfrei)
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    Series: [Stanford University Graduate School of Business research paper ; no. 18, 34]
    Scope: 1 Online-Ressource (circa 39 Seiten), Illustrationen
  2. Demand estimation with text and image data
    Author: Compiani, Giovanni; Morozov, Ilya; Seiler, Stephan
    Published: 07 October 2023
    Publisher:  Centre for Economic Policy Research, London

    Access:
    Verlag (lizenzpflichtig)
    Verlag (lizenzpflichtig)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    LZ 161
    Inter-library loan:
    No inter-library loan

    Location:
    Universitätsbibliothek Mannheim
    Inter-library loan:
    No inter-library loan
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Array ; DP18507
    Subjects: Demand Estimation; Unstructured Data; Computer Vision; Text Models
    Scope: 1 Online-Ressource (circa 31 Seiten), Illustrationen
  3. 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:
    No inter-library loan

     

    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
      BibTeX file
    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