Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood (ML) estimation. An alternative approach, pseudolikelihood (PL) estimation, which decouples estimation of the error component structure and the production frontier, has been adopted in both the non-parametric and panel data settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross-sectional framework. We produce a comparison of these two competing methods using Monte Carlo simulations. Our results indicate that PL estimation enjoys almost identical performance to ML estimation across a range of scenarios and performance metrics, and for certain metrics, outperforms ML estimation when the distribution of inefficiency is incorrectly specified.