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Quantile regression for Panel data: An empirical approach for knowledge spillovers endogeneity

Aldieri, Luigi and Vinci, Concetto Paolo (2017): Quantile regression for Panel data: An empirical approach for knowledge spillovers endogeneity.

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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 and 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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