@ARTICLE{Makieła_Kamil_Bayesian_2014, author={Makieła, Kamil}, number={No 3}, journal={Central European Journal of Economic Modelling and Econometrics}, pages={193-216}, howpublished={online}, year={2014}, publisher={Oddział PAN w Łodzi}, abstract={The paper discusses Bayesian productivity analysis of 27 EU Member States, USA, Japan and Switzerland. Bayesian Stochastic Frontier Analysis and a two-stage structural decomposition of output growth are used to trace sources of output growth. This allows us to separate the impacts of capital accumulation, labour growth, technical progress and technical efficiency change on economic development. Since estimates of the growth components are conditioned upon model parameterisation and the underlying assumptions, a number of possible specifications are considered. The best model for decomposing output growth is chosen based on the highest marginal data density, which is calculated using adjusted harmonic mean estimator.}, type={Artykuły / Articles}, title={Bayesian Stochastic Frontier Analysis of Economic Growth and Productivity Change in the EU, USA, Japan and Switzerland}, URL={http://www.journals.pan.pl/Content/103743/PDF-MASTER/mainFile.pdf}, doi={10.24425/cejeme.2014.119239}, keywords={stochastic frontier analysis, Bayesian inference, productivity analysis, economic growth decomposition}, }