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So close and so far. Finding similar tendencies in econometrics and machine learning papers. Topic models comparison

Author

Listed:
  • Marcin Chlebus

    (Faculty of Economic Sciences, University of Warsaw)

  • Maciej Stefan Świtała

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The paper takes into consideration the broad idea of topic modelling and its application. The aim of the research was to identify mutual tendencies in econometric and machine learning abstracts. Different topic models were compared in terms of their performance and interpretability. The former was measured with a newly introduced approach. Summaries collected from esteemed journals were analysed with LSA, LDA and CTM algorithms. The obtained results enable finding similar trends in both corpora. Probabilistic models – LDA and CTM – outperform the semantic alternative – LSA. It appears that econometrics and machine learning are fields that consider problems being rather homogenous at the level of concept. However, they differ in terms of used tools and dominance in particular areas.

Suggested Citation

  • Marcin Chlebus & Maciej Stefan Świtała, 2020. "So close and so far. Finding similar tendencies in econometrics and machine learning papers. Topic models comparison," Working Papers 2020-16, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2020-16
    as

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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/5660/
    File Function: First version, 2020
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    References listed on IDEAS

    as
    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    More about this item

    Keywords

    abstracts; comparison; interpretability; tendencies; topics;
    All these keywords.

    JEL classification:

    • A12 - General Economics and Teaching - - General Economics - - - Relation of Economics to Other Disciplines
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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