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We have used a Bayesian semiparametric regression approach to model the probability of an opposition against European Patent Office patents from the biotechnology/ pharmaceutical and semiconductor/computer software sectors

Modeling probabilities of patent oppositions in a Bayesian semiparametric regression framework

Empirical Economics, no. 2 (2006): 513-533

Cited by: 12|Views2

Abstract

Previous econometric analyses of patent data rely on regression methods using purely parametric forms of the predictor for modeling the dependence of the response. These approaches lack the capability of identifying potential non-linear relationships between dependent and independent variables. In this paper, we present a Bayesian semipar...More

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Introduction
  • The authors apply a semiparametric approach described in Fahrmeir and Lang (2001b) and Brezger and Lang (2005) to analyze the determinants and the effects of

    A.
  • The authors apply a semiparametric approach described in Fahrmeir and Lang (2001b) and Brezger and Lang (2005) to analyze the determinants and the effects of.
  • This approach replaces linear effects x′β of metrical covariates x by smooth regression functions f(x).
  • In order to analyze the benefits from applying semiparametric models the authors compare the 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
Highlights
  • In this paper, we apply a semiparametric approach described in Fahrmeir and Lang (2001b) and Brezger and Lang (2005) to analyze the determinants and the effects of

    A
  • In order to analyze the benefits from applying semiparametric models we compare our specification to the results of a simple linear probit model employed by Graham et al (2002) using their dataset on European Patent Office (EPO) patents from the biotechnology/pharmaceutical and semiconductor/computer software sector
  • As our main focus is to show that a semiparametric regression approach does have clear benefits compared to a simple linear probit model, we only present the results for the most important covariates described in Graham et al (2002), omitting the indicators for a Japanese patent holder and for an independent inventor which we found to be insignificant
  • We have used a Bayesian semiparametric regression approach to model the probability of an opposition against EPO patents from the biotechnology/ pharmaceutical and semiconductor/computer software sectors
  • We were able to show that this increase was clearly non-linear by incorporating the effects of these metrical covariates in form of smooth regression functions instead of simple linear terms
  • The model validation revealed that the chosen estimation strategy performed better than purely parametric estimations in both explaining and predicting the occurrence of opposition
Results
  • According to the annual reports of the EPO, about 65 to 70% of the applications at the EPO are granted.
  • Considering the EPO, any third party can oppose a patent by filing and substantiating an opposition within nine months after the grant decision, which is the case for about 8 to 10% of all granted patents (Harhoff and Wagner 2003)
Conclusion
  • Conclusions and outlook

    In this paper, the authors have used a Bayesian semiparametric regression approach to model the probability of an opposition against EPO patents from the biotechnology/ pharmaceutical and semiconductor/computer software sectors.
  • Due to the hierarchical structure of the Bayesian approach, the smoothness of the estimated functions is totally data-driven and estimated jointly with the unknown regression parameters not requiring any prior specifications of smoothness parameters or functional forms
  • This makes the chosen approach a valuable tool for the analysis of complex dependency structures, which are present in patent data and in other fields like the modeling of credit defaults or insurance claims.
  • The semiparametric approach presented here might be a first step in a refined modelling of the underlying dependency structures
Summary
  • Introduction:

    The authors apply a semiparametric approach described in Fahrmeir and Lang (2001b) and Brezger and Lang (2005) to analyze the determinants and the effects of

    A.
  • The authors apply a semiparametric approach described in Fahrmeir and Lang (2001b) and Brezger and Lang (2005) to analyze the determinants and the effects of.
  • This approach replaces linear effects x′β of metrical covariates x by smooth regression functions f(x).
  • In order to analyze the benefits from applying semiparametric models the authors compare the 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
  • Objectives:

    The authors' aim is to model the probability πi that an opposition against a granted patent occurs yielding the binary response variable yi 1⁄4 1 , Opposition yi 1⁄4 0 , No opposition.
  • Results:

    According to the annual reports of the EPO, about 65 to 70% of the applications at the EPO are granted.
  • Considering the EPO, any third party can oppose a patent by filing and substantiating an opposition within nine months after the grant decision, which is the case for about 8 to 10% of all granted patents (Harhoff and Wagner 2003)
  • Conclusion:

    Conclusions and outlook

    In this paper, the authors have used a Bayesian semiparametric regression approach to model the probability of an opposition against EPO patents from the biotechnology/ pharmaceutical and semiconductor/computer software sectors.
  • Due to the hierarchical structure of the Bayesian approach, the smoothness of the estimated functions is totally data-driven and estimated jointly with the unknown regression parameters not requiring any prior specifications of smoothness parameters or functional forms
  • This makes the chosen approach a valuable tool for the analysis of complex dependency structures, which are present in patent data and in other fields like the modeling of credit defaults or insurance claims.
  • The semiparametric approach presented here might be a first step in a refined modelling of the underlying dependency structures
Tables
  • Table1: EPO patent opposition (full data): summary of metrical variables together with empirical p quantiles xp as well as definitions and absolute frequencies of occurrence for categorized versions
  • Table2: EPO patent opposition (full data): summary of binary variables together with absolute frequencies of occurrence
  • Table3: EPO patent opposition (training data): results for M1
  • Table4: EPO patent opposition (training/validation data): deviance (Dev), effective number of model parameters (pD), deviance information criterion (DIC), prediction error (Err) and area under the ROC curve (AUC) for M1, ..., M3
Download tables as Excel
Funding
  • This research was supported by the German National Science Foundation (DFG), Sonderforschungsbereich 386 “Statistical Analysis of Discrete Structures”
Study subjects and analysis
cases: 3
For M3 a nonparametric regression function with a Pspline approach described in more detail in Section 3.2 is used. The parameter estimation in all three cases is fully Bayesian and will be explained in Section 3.2. Figure 1 shows the estimated probabilities for M1, ..., M3 and reveals that only the semiparametric approach M3 is capable of detecting the drop in opposition rate for 12 to 14 designated states

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