Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/142377
Authors: 
Gimenez-Nadal, J. Ignacio
Lafuente, Miguel
Molina, José Alberto
Velilla, Jorge
Year of Publication: 
2016
Series/Report no.: 
IZA Discussion Papers 9938
Abstract: 
In this paper, we propose an algorithmic approach based on resampling and bootstrap techniques to measuring the importance of a variable, or a set of variables, in econometric models. This algorithmic approach allows us to check the real weight of a variable in a model, avoiding the biases of classical tests, and to select the more powerful variables, or more relevant models, in terms of predictability, reducing dimensions. We apply this methodology to the Global Entrepreneurship Monitor data for the year 2014, and find that innovation and new technologies, help others with their business, and that entrepreneurial education at University and the availability of government subsidies, are among the most important predictors for entrepreneurial behavior.
Subjects: 
bootstrap
regression
classification
entrepreneurship data
JEL: 
C21
C52
Document Type: 
Working Paper

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