Quantile Regression for Panel Data: An Empirical Approach for Knowledge Spillovers Endogeneity
Luigi Aldieri () and
Concetto Paolo Vinci
International Journal of Economics and Finance, 2017, vol. 9, issue 7, 106-113
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.
Keywords: innovation; spillovers; quantile regression; knowledge diffusion (search for similar items in EconPapers)
JEL-codes: R00 Z0 (search for similar items in EconPapers)
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Working Paper: Quantile regression for Panel data: An empirical approach for knowledge spillovers endogeneity (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ibn:ijefaa:v:9:y:2017:i:7:p:106-113
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