Catching Gazelles with a Lasso: Big Data Techniques for the Prediction of High-Growth Firms
79 Pages Posted: 12 Jun 2019
Date Written: April 3, 2019
We investigate whether our limited ability to predict high-growth firms 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
JEL Classification: L25
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