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

Alex Coad*, Stjepan Srhoj

*この研究の対応する著者

研究成果査読

21 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)541-565
ページ数25
ジャーナルSmall Business Economics
55
3
DOI
出版ステータスPublished - 2020 10 1
外部発表はい

ASJC Scopus subject areas

  • ビジネス、管理および会計(全般)
  • 経済学、計量経済学

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