Most econometric analyses of patent data rely on regression methods using a parametric form of the predictor for modeling the dependence of the response given certain covariates. These methods often lack the capability of identifying non-linear relationships between dependent and independent variables. We present an approach based on a generalized additive model in order to avoid these shortcomings. Our method is fully Bayesian and makes use of Markov Chain Monte Carlo (MCMC) simulation techniques for estimation purposes. Using this methodology we reanalyze the determinants of patent oppositions in Europe for biotechnology/pharmaceutical and semiconductor/computer software patents. Our results largely confirm the findings of a previous parametric analysis of the same data provided by Graham, Hall, Harhoff & Mowery (2002). However, our model specification clearly verifies considerable non-linearities in the effect of various metrical covariates on the probability of an opposition. Furthermore, our semiparametric approach shows that the categorizations of these covariates made by Graham et al. (2002) cannot capture those non-linearities and, from a statistical point of view, appear to somehow ad hoc.
Markov Chain Monte Carlo Bayesian semiparametric binary regression Latent utility models Bayesian P-splines Patent opposition