Quantile Regression for Panel Data: An Empirical Approach for Knowledge Spillovers Endogeneity

Luigi Aldieri, Concetto Paolo Vinci

Abstract


The aim of this paper is to investigate the extent to which knowledge spillovers effects are sensitive to different levels of innovation. We develop a theoretical model in which the core of spillover effect is showed and then we implement the empirical model to test for the results. In particular, we run the quantile regression for panel data estimator (Baker, Powell, & Smith, 2016), to correct the bias stemming from the endogenous regressors in a panel data sample. The findings identify a significant heterogeneity of technology spillovers across quantiles: the highest value of spillovers is observed at the lowest quartile of innovation distribution. The results might be interpreted to provide some useful implications for industrial policy strategy.


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DOI: https://doi.org/10.5539/ijef.v9n7p106

Copyright (c) 2017 Luigi Aldieri, Concetto Paolo Vinci

License URL: http://creativecommons.org/licenses/by/4.0

International Journal of Economics and Finance  ISSN  1916-971X (Print) ISSN  1916-9728 (Online)  Email: ijef@ccsenet.org

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