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  1. Inference for regression with variables generated from unstructured data
    Author: Battaglia, Laura; Christensen, Tim; Hansen, Stephen; Sacher, Szymon
    Published: May 2024
    Publisher:  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... more

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 63
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    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.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/300047
    Series: CESifo working papers ; 11119 (2024)
    Subjects: unstructured data; information retrieval; topic modeling; Hamiltonian Monte Carlo; measurement error
    Scope: 1 Online-Ressource (circa 61 Seiten), Illustrationen
  2. Identification of innovation drivers based on technology-related news articles
    Author: Latifi, Albina; Lenz, David; Winker, Peter
    Published: [2024]
    Publisher:  Philipps-University Marburg, School of Business and Economics, Marburg

    Innovations contribute to economic growth. Hence, knowledge about drivers of innovation activities is a necessary input for economic policy making when it comes to implement targeted support measures. We focus on firms as potential drivers of... more

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    Verlag (kostenfrei)
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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 102
    Inter-library loan:
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    Innovations contribute to economic growth. Hence, knowledge about drivers of innovation activities is a necessary input for economic policy making when it comes to implement targeted support measures. We focus on firms as potential drivers of innovation and use a novel data-driven approach to identify them. The approach is based on news articles from a technology-related newspaper for the period 1996-2021. In a first step, natural language processing (NLP) tools are used to identify latent topics in the text corpus. Expert knowledge is used to tag innovation-related topics. In a second step, a named entity recognition (NER) method is used to detect firm names in the news articles. Combining the information about innovation-related topics and firms mentioned in news articles linked to these topics provides a set of firms linked to each innovation-related topic. The results suggest that the approach helps identifying drivers of innovation activities going beyond the usual suspects. However, given that the rate of false alarms is not negligible, at the end also human judgement is needed when using this approach.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/283532
    Series: Joint discussion paper series in economics ; no. 2024, 01
    Subjects: nnovation drivers; topic modeling; entity recognition
    Scope: 1 Online-Ressource (circa 42 Seiten), Illustrationen
  3. Enhancing literature review with NLP methods algorithmic investment strategies case
    Author: Łaniewski, Stanisław; Ślepaczuk, Robert
    Published: 2024
    Publisher:  University of Warsaw, Faculty of Economic Sciences, Warsaw

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    Verlag (kostenfrei)

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Working papers / University of Warsaw, Faculty of Economic Sciences ; no. 2024, 16 = 452
    Subjects: trading; quantitative finance; neural networks; literature review; knowledge representation; natural language processing (NLP); topic modeling; model comparison; artificial intelligence
    Scope: 1 Online-Ressource (circa 36 Seiten), Illustrationen