Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms
Alex Coad () and
Small Business Economics, 2020, vol. 55, issue 3, No 2, 565 pages
Abstract 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.
Keywords: LASSO; High-growth firms; Prediction; Within variation; Firm growth; Post hoc interpretation; Inventories (search for similar items in EconPapers)
JEL-codes: L25 L26 (search for similar items in EconPapers)
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