The components of output growth: A stochastic frontier analysis

This paper uses Bayesian stochastic frontier methods to decompose output ch ange into technical, efficiency and input changes. In the context of macroe conomic,growth exercises, which typically involve small and noisy data sets , we argue that stochastic frontier methods are useful since they incorpora te measurement error and assume a (flexible) parametric form for the produc tion relationship. These properties enable us to calculate measures of unce rtainty associated with the decomposition and minimize the risk of overfitt ing 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.