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17-06-2019 | Issue 3/2020

Small Business Economics 3/2020

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

Journal:
Small Business Economics > Issue 3/2020
Authors:
Alex Coad, Stjepan Srhoj
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Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s11187-019-00203-3) contains supplementary material, which is available to authorized users.

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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.

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