Semi-parametric analysis of efficiency and productivity using Gaussian processes [Estimation of long-run inefficiency levels: A dynamic frontier approach]
提出一种基于高斯过程的完全贝叶斯半参数方法，无需预设生产前沿函数形式，在模拟数据中表现良好，并应用于美国电力企业面板数据分解全要素生产率增长。
SummaryThis paper proposes a fully Bayesian semi-parametric method for efficiency and productivity analysis based on Gaussian processes. The proposed technique frees the researcher from having to specify a functional form for the production frontier, and it is shown in simulated data to perform as well as flexible parametric models when correct distributional assumptions are imposed on the inefficiency component of the error term, and slightly better when incorrect assumptions are made. The technique is applied to a panel dataset of US electric utilities, where total-factor productivity growth is estimated and decomposed with both parametric and semi-parametric techniques.