Pregled bibliografske jedinice broj: 1001617
Catching Gazelles with a Lasso: Big Data Techniques for the Prediction of High-Growth Firms
Catching Gazelles with a Lasso: Big Data Techniques for the Prediction of High-Growth Firms // Small business economics, 55 (2020), 541-565 doi:10.1007/s11187-019-00203-3 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1001617 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Catching Gazelles with a Lasso: Big Data Techniques
for the Prediction of High-Growth Firms
Autori
Coad, Alex ; Srhoj, Stjepan
Izvornik
Small business economics (0921-898X) 55
(2020);
541-565
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
LASSO ; High-growth firms ; prediction ; within variation ; firm growth ; post-hoc interpretation ; inventories
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Ekonomija
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Social Science Citation Index (SSCI)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus
- EconLit
