The Components of Output Growth: A Stochastic Frontier Analysis
用贝叶斯随机前沿方法将产出变化分解为技术变化、效率变化和投入变化，适用于数据少且噪声大的宏观经济研究，能计算分解的不确定性并避免过拟合。
This paper uses Bayesian stochastic frontier methods to decompose output change into technical, efficiency and input changes. In the context of macroeconomic growth exercises, which typically involve small and noisy data sets, we argue that stochastic frontier methods are useful since they incorporate measurement error and assume a (flexible) parametric form for the production relationship. These properties enable us to calculate measures of uncertainty associated with the decomposition and minimize the risk of overfitting the noise in the data. Tools for Bayesian inference in such models are developed. An empirical investigation using data from 17 OECD countries for 10 years illustrates the practicality and usefulness of our approach.