Modeling probabilities of patent oppositions in a Bayesian semiparametric regression framework
Modeling probabilities of patent oppositions in a Bayesian semiparametric regression framework
复制标题
贝叶斯半参数回归框架中的专利异议概率建模
DOI：
10.1007/s00181-005-0047-0
复制
发表时间：
2006
影响因子：
3.2
通讯作者：
Stefan Wagner
中科院分区：
文献类型：
--
作者：
Alexander Jerak;Stefan Wagner
以前对专利数据的计量经济学分析依赖于使用纯参数形式的预测变量的回归方法来对响应的依赖性进行建模。这些方法缺乏识别因变量和自变量之间潜在非线性关系的能力。在本文中，我们提出了一种利用马尔可夫链蒙特卡罗（MCMC）模拟技术的贝叶斯半参数方法，该方法能够捕获这些非线性。使用这种方法，我们重新分析了欧洲生物技术/制药和半导体/计算机软件专利专利异议的决定因素。我们的半参数规范清楚地发现各种度量协变量的影响存在相当大的非线性，这是之前没有讨论过的。此外，基于 ROC 方法的正式模型验证将训练数据和验证数据集的数据分开，表明与纯参数规范相比，我们的方法的解释性和预测能力有了显着提高。
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 semiparametric approach making use of Markov Chain Monte Carlo (MCMC) simulation techniques which is able to capture these non-linearities. Using this methodology we reanalyze the determinants of patent oppositions in Europe for biotechnology/pharmaceutical and semiconductor/computer software patents. Our semiparametric specification clearly finds considerable non-linearities in the effect of various metrical covariates which has been not been discussed previously. Further, a formal model validation based on ROC-methodology which splits the data in a training and a validation data set shows a significant improvement of the explanatory and the predictive power of our approach compared to purely parametric specifications.