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Empirical Economics

, Volume 56, Issue 1, pp 233–267 | Cite as

Resampling and bootstrap algorithms to assess the relevance of variables: applications to cross section entrepreneurship data

  • Jose Ignacio Gimenez-Nadal
  • Miguel Lafuente
  • Jose Alberto Molina
  • Jorge Velilla
Article

Abstract

In this paper, we propose an algorithmic approach based on resampling and bootstrap techniques to measure 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 relevant variables, or models, in terms of predictability, by reducing dimensions. We apply this methodology to the Global Entrepreneurship Monitor data for the year 2014, to analyze the individual- and national-level determinants of entrepreneurial activity, and compare the results with a forward selection approach, also based on resampling predictability, and a standard forward stepwise selection process. We find that our proposed techniques offer more accurate results, which show that innovation and new technologies, peer effects, the sociocultural environment, entrepreneurial education at University, R&D transfers, and the availability of government subsidies are among the most important predictors of entrepreneurial behavior.

Keywords

Bootstrap Regression Logit GEM data Entrepreneurship 

JEL Classification

C21 C52 

Notes

Acknowledgements

This paper has benefited from funding from the Spanish Ministry of Economics (Project ECO2012-34828). Specifically, ML acknowledges the support from project MTM-2014-53340-P. ML is member of the research group “Modelos Estocásticos,” supported by DGA and the European Social Fund.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Jose Ignacio Gimenez-Nadal
    • 1
  • Miguel Lafuente
    • 1
  • Jose Alberto Molina
    • 1
    • 2
  • Jorge Velilla
    • 1
  1. 1.University of ZaragozaZaragozaSpain
  2. 2.IZABonnGermany

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