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

An Integrated Impact Indicator (I3): A New Definition of "Impact" with Policy Relevance

Allocation of research funding, as well as promotion and tenure decisions, are increasingly made using indicators and impact factors drawn from citations to published work. A debate among scientometricians about proper normalization of citation counts has resolved with the creation of an Integrated Impact Indicator (I3) that solves a number of problems found among previously used indicators. The I3 applies non-parametric statistics using percentiles, allowing highly-cited papers to be weighted more than less-cited ones. It further allows unbundling of venues (i.e., journals or databases) at the article level. Measures at the article level can be re-aggregated in terms of units of evaluation. At the venue level, the I3 creates a properly weighted alternative to the journal impact factor. I3 has the added advantage of enabling and quantifying classifications such as the six percentile rank classes used by the National Science Board's Science & Engineering Indicators.Comment: Research Evaluation (in press