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. Demand estimation with text and image data
    Autor*in: Compiani, Giovanni; Morozov, Ilya; Seiler, Stephan
    Erschienen: October 2023
    Verlag:  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... mehr

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

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 63
    Fernleihe:
    keine Fernleihe

     

    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 in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/282383
    Schriftenreihe: CESifo working papers ; 10695 (2023)
    Schlagworte: demand estimation; unstructured data; computer vision; text models
    Umfang: 1 Online-Ressource (circa 30 Seiten), Illustrationen
  2. Inference for regression with variables generated from unstructured data
    Autor*in: Battaglia, Laura; Christensen, Timothy; Hansen, Stephen; Sacher, Szymon
    Erschienen: May 2024
    Verlag:  CESifo, Munich, Germany

    The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as “data” in a downstream econometric... mehr

    Zugang:
    Verlag (kostenfrei)
    Verlag (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 63
    Fernleihe:
    keine Fernleihe

     

    The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as “data” in a downstream econometric model. We establish theoretical arguments for why this two-step strategy leads to biased inference in empirically plausible settings. More constructively, we propose a one-step strategy for valid inference that uses the upstream and downstream models jointly. The one-step strategy (i) substantially reduces bias in simulations; (ii) has quantitatively important effects in a leading application using CEO time-use data; and (iii) can be readily adapted by applied researchers.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: CESifo working papers ; 11119 (2024)
    Schlagworte: unstructured data; information retrieval; topic modeling; Hamiltonian Monte Carlo; measurement error
    Umfang: 1 Online-Ressource (circa 61 Seiten), Illustrationen