Modeling 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 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.