engleski

Catching Gazelles with a Lasso: Big Data Techniques for the Prediction of High-Growth Firms

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 R 2 statistics of around 10% indicate that HGF prediction remains a challenging exercise.

LASSO ; High-growth firms ; prediction ; within variation ; firm growth ; post-hoc interpretation ; inventories

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