A comparison of Data Envelopment Analysis and the stochastic frontier model using panel data

Two competing approaches for the measurement of efficiency are the stochastic frontier model and data envelopment analysis (DEA). Previous research has established that the two models applied to cross-sectional data are both adversely affected by measurement error. While the cross-sectional stochastic frontier model does not effectively handle statistical noise, panel data models do. This is true because additional information from multiple time periods is incorporated into the estimation. A panel data DEA model that uses averaged data has been shown to effectively smooth out measurement error. In this paper, we compare the panel data models using simulated data.