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Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms

Small business economics, 2020-10, Vol.55 (3), p.541-565 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;Springer Science+Business Media, LLC, part of Springer Nature 2019. ;ISSN: 0921-898X ;EISSN: 1573-0913 ;DOI: 10.1007/s11187-019-00203-3

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  • Title:
    Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms
  • Author: Coad, Alex ; Srhoj, Stjepan
  • Subjects: Big Data ; Business and Management ; Business growth ; Catching ; Companies ; Employment ; Entrepreneurship ; Industrial Organization ; Management ; Microeconomics ; Predictions
  • Is Part Of: Small business economics, 2020-10, Vol.55 (3), p.541-565
  • Description: We investigate whether our limited ability to predict high-growth firms (HGF) is because previous research has used a restricted set of explanatory variables, and in particular because there is a need for explanatory variables with high variation within firms over time. To this end, we apply “big data” techniques (i.e., LASSO; Least Absolute Shrinkage and Selection Operator) to predict HGFs in comprehensive datasets on Croatian and Slovenian firms. Firms with low inventories, higher previous employment growth, and higher short-term liabilities are more likely to become HGFs. Pseudo-R² statistics of around 10% indicate that HGF prediction remains a challenging exercise.
  • Publisher: New York: Springer Science + Business Media
  • Language: English
  • Identifier: ISSN: 0921-898X
    EISSN: 1573-0913
    DOI: 10.1007/s11187-019-00203-3
  • Source: ProQuest Central
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