Estimating Probabilities of Patent Oppositions in a Bayesian Semiparametric Regression Framework

Most econometric analyses of patent data rely on regression methods using a parametric form of the predictor for modeling the dependence of the response in focus on given 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 and the effects 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 verifes considerable non-linearities in the efect of various covariates on the probability of an opposition. Furthermore, our semiparametric approach shows that some categorizations of metric covariates made by Graham et al. (2002) in order to capture those non-linearities appear to be somehow ad hoc and could be optimized