Resampling and Bootstrap to Assess the Relevance of Variables: A New Algorithmic Approach with Applications to Entrepreneurship Data - J. Ignacio Gimenez-Nadal; Miguel Lafuente; José Alberto Molina; Jorge Velilla (Institute for the Study of Labor (IZA), 2016)
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: C21; C52; 330; bootstrap; regression; classification; entrepreneurship data