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

Alex Coad, Stjepan Srhoj

研究成果: Article

8 引用 (Scopus)

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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.

元の言語English
ページ(範囲)541-565
ページ数25
ジャーナルSmall Business Economics
55
発行部数3
DOI
出版物ステータスAccepted/In press - 2019

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics

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