Abstract
In this paper, we apply a semiparametric approach described in Fahrmeir & Lang (2001b) and Brezger & Lang (2005) to analyze the determinants and the effects of patent oppositions in Europe. This approach replaces linear effects χ′β of metrical covariates χ by smooth regression functions f(χ). Within a Bayesian framework we apply MCMC-methods for estimation purposes. In order to analyze the benefits from applying semi-parametric models we compare our specification to the results of a simple linear probit model employed by Graham et al. (2002) using their dataset on EPO patents from the biotechnology/pharmaceutical and semiconductor/computer software sector.
This Chapter is joint work with Alexander Jerak. It has been accepted for pubhcation and is forthcoming under the same title in Empirical Economics. Participants at the CEPR/IESE conference on ‘The Impact of Institutions on Innovations’ in Barcelona (2003) and at ‘CompStat2004’ in Prague provided helpful comments. We would also like to thank two anonymous referees for their valuable comments that helped to improve our presentation as well as Ludwig Fahrmeir and Dietmar Harhoff for helpful discussions.
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(2006). Modeling Probabilities of Patent Oppositions in a Bayesian Semiparametric Regression Framework. In: Economic Analyses of the European Patent System. DUV. https://doi.org/10.1007/978-3-8350-9050-7_2
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DOI: https://doi.org/10.1007/978-3-8350-9050-7_2
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