We propose a Bayesian one-stage approach to estimate the effect of inefficiency on the time to failure (bankruptcy) of U.S. commercial banks. We do so combining stochastic frontier and proportional hazards settings. Most of the existing literature use two-stage methods which may yield inefficient, biased, and inconsistent estimates. Our proposal overcomes these issues, allows computing the marginal distribution of inefficiencies for each observational unit, and facilitates statistical inference of non-linear functions of parameters such as returns to scale. Simulation exercises show that our proposal outperforms the two-stage maximum likelihood approach traditionally used in the literature. In addition, empirical evidence suggests that inefficiency of U.S. commercial banks during the global financial crisis in 2008–2009 played a statistically and economically significant role determining the time to failure.
