Resampling and Bootstrap to Assess the Relevance of Variables: A New Algorithmic Approach with Applications to Entrepreneurship Data
J. Ignacio Gimenez-Nadal (),
José Alberto Molina () and
Jorge Velilla ()
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J. Ignacio Gimenez-Nadal: University of Zaragoza
Miguel Lafuente: University of Zaragoza
Authors registered in the RePEc Author Service: Jose Ignacio Gimenez-Nadal ()
No 9938, IZA Discussion Papers from Institute of Labor Economics (IZA)
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.
Keywords: classification; regression; bootstrap; entrepreneurship data (search for similar items in EconPapers)
JEL-codes: C21 C52 (search for similar items in EconPapers)
Pages: 35 pages
New Economics Papers: this item is included in nep-ent and nep-sbm
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Published as “Resampling and bootstrap algorithms to asses the relevance of variables: applications to cross-section entrepreneurship data” in: Empirical Economics, 2019, 56, 233-267
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