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  1. Hedonic prices and quality adjusted price indices powered by AI
    Autor*in: Bajari, Patrick L.; Cen, Zhihao; Chernozhukov, Victor; Manukonda, Manoj; Wang, Jin; Huerta, Ramon; Li, Junbo; Leng, Ling; Monokroussos, George; Vijaykunar, Suhas; Wan, Shan
    Erschienen: [2021]
    Verlag:  Cemmap, Centre for Microdata Methuods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    We develop empirical models of hedonic prices and derive hedonic indices for measuring changes in customer welfare based upon deep learning. We first generate abstract product attributes, or "features," from text descriptions and images using deep... mehr

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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 243
    Fernleihe:
    keine Fernleihe

     

    We develop empirical models of hedonic prices and derive hedonic indices for measuring changes in customer welfare based upon deep learning. We first generate abstract product attributes, or "features," from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function. Specifically, we convert textual information about the product to numeric product features using the ELMO or BERT language models, trained or fine-tuned using Amazon's product descriptions. We convert the product image to numerical product features by a pre-trained ResNet50 image model. To produce the estimated hedonic price function, we use a multi-task neural network again, trained to predict the price of a product simultaneously in all time periods. We apply the models to Amazon's data for first-party apparel sales to estimate hedonic prices. The resulting models have high predictive accuracy, with R2 ranging from 80% to 90%. We also construct hedonic price indices: over the period 2013-2017 the hedonic Fisher price index decreased, providing improvement in customer welfare.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/241940
    Auflage/Ausgabe: This version: February 20, 2021
    Schriftenreihe: Cemmap working paper ; CWP21, 04
    Umfang: 1 Online-Ressource (circa 52 Seiten), Illustrationen
  2. Hedonic prices and quality adjusted price indices powered by AI
    Autor*in: Bajari, Patrick L.; Cen, Zhihao; Chernozhukov, Victor; Manukonda, Manoj; Wang, Jin; Huerta, Ramon; Li, Junbo; Leng, Ling; Monokroussos, George; Vijaykunar, Suhas; Wan, Shan
    Erschienen: [2023]
    Verlag:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    Accurate, real-time measurements of price index changes using electronic records are essential for tracking inflation and productivity in today's economic environment. We develop empirical hedonic models that can process large amounts of unstructured... mehr

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

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 243
    Fernleihe:
    keine Fernleihe

     

    Accurate, real-time measurements of price index changes using electronic records are essential for tracking inflation and productivity in today's economic environment. We develop empirical hedonic models that can process large amounts of unstructured product data (text, images, prices, quantities) and output accurate hedonic price estimates and derived indices. To accomplish this, we generate abstract product attributes, or "features," from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function. Specifically, we convert textual information about the product to numeric features using large language models based on transformers, trained or fine-tuned using product descriptions, and convert the product image to numeric features using a residual network model. To produce the estimated hedonic price function, we again use a multi-task neural network trained to predict a product's price in all time periods simultaneously. To demonstrate the performance of this approach, we apply the models to Amazon's data for first-party apparel sales and estimate hedonic prices. The resulting models have high predictive accuracy, with R 2 ranging from 80% to 90%. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency. We contrast the index with the CPI and other electronic indices.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/284132
    Auflage/Ausgabe: This version: April 20, 2023
    Schriftenreihe: Cemmap working paper ; CWP23, 08
    Schlagworte: Hedonic Prices; Price Index; Transformers; Deep Learning; Artificial Intelligence
    Umfang: 1 Online-Ressource (circa 48 Seiten), Illustrationen