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Pregled bibliografske jedinice broj: 982816

The role of entrepreneurship in explaining the real Gross Domestic Product per capita : regression model selection


Harmina, Anita
The role of entrepreneurship in explaining the real Gross Domestic Product per capita : regression model selection // Croatian Review of Economic, Business and Social Statistics (CREBSS), 2 (2016), 1; 24-38 doi:10.1515/crebss-2016-0007 (međunarodna recenzija, članak, znanstveni)


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Naslov
The role of entrepreneurship in explaining the real Gross Domestic Product per capita : regression model selection

Autori
Harmina, Anita

Izvornik
Croatian Review of Economic, Business and Social Statistics (CREBSS) (1849-8531) 2 (2016), 1; 24-38

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Akaike information criterion ; correlation ; double-logarithmic regression model ; entrepreneurship ; multiple linear regression model

Sažetak
In this research, the impact of total early- stage entrepreneurial activity and competitiveness of the economy on the real gross domestic product (GDP) per capita is analyzed in a cross-section of world economies using the methods of correlation and multiple regression analysis. In the attempt to select between the linear and the double-logarithmic model, the regression diagnostics and quality of the relationship between the dependent and the independent variables were analyzed. The functional form of the model was tested by the MacKinnon, White and Davidson test. Model selection methods regarding the comparison of coefficients of determination and the Akaike information criterion were used. The results of the analysis show that independent variables have a statistically significant impact on the real GDP per capita, and that the real GDP per capita is elastic to the changes of competitiveness but inelastic to the changes of total early-stage entrepreneurial activity.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Ekonomija



POVEZANOST RADA


Ustanove:
Sveučilište VERN, Zagreb

Profili:

Avatar Url Anita Harmina (autor)

Poveznice na cjeloviti tekst rada:

doi hrcak.srce.hr content.sciendo.com

Citiraj ovu publikaciju:

Harmina, Anita
The role of entrepreneurship in explaining the real Gross Domestic Product per capita : regression model selection // Croatian Review of Economic, Business and Social Statistics (CREBSS), 2 (2016), 1; 24-38 doi:10.1515/crebss-2016-0007 (međunarodna recenzija, članak, znanstveni)
Harmina, A. (2016) The role of entrepreneurship in explaining the real Gross Domestic Product per capita : regression model selection. Croatian Review of Economic, Business and Social Statistics (CREBSS), 2 (1), 24-38 doi:10.1515/crebss-2016-0007.
@article{article, author = {Harmina, Anita}, year = {2016}, pages = {24-38}, DOI = {10.1515/crebss-2016-0007}, keywords = {Akaike information criterion, correlation, double-logarithmic regression model, entrepreneurship, multiple linear regression model}, journal = {Croatian Review of Economic, Business and Social Statistics (CREBSS)}, doi = {10.1515/crebss-2016-0007}, volume = {2}, number = {1}, issn = {1849-8531}, title = {The role of entrepreneurship in explaining the real Gross Domestic Product per capita : regression model selection}, keyword = {Akaike information criterion, correlation, double-logarithmic regression model, entrepreneurship, multiple linear regression model} }
@article{article, author = {Harmina, Anita}, year = {2016}, pages = {24-38}, DOI = {10.1515/crebss-2016-0007}, keywords = {Akaike information criterion, correlation, double-logarithmic regression model, entrepreneurship, multiple linear regression model}, journal = {Croatian Review of Economic, Business and Social Statistics (CREBSS)}, doi = {10.1515/crebss-2016-0007}, volume = {2}, number = {1}, issn = {1849-8531}, title = {The role of entrepreneurship in explaining the real Gross Domestic Product per capita : regression model selection}, keyword = {Akaike information criterion, correlation, double-logarithmic regression model, entrepreneurship, multiple linear regression model} }

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