Enhancing Efficiency: Halton Draws in the Generalized True Random Effects Model - David H. Bernstein (Econometrics, 2024)
This paper measures the impact of the number of Halton draws in excess of <semantics>⌈<msqrt>n</msqrt>⌉</semantics> on technical efficiency in the generalized true random effects (four-component) stochastic frontier model estimated by simulated maximum likelihood. A substantial set of Monte Carlo simulations demonstrates that increasing the number of Halton draws to <semantics>⌈n3/4⌉</semantics> (<semantics>⌈n2/3⌉</semantics>) decreases the mean squared error of the total technical efficiency estimates by <semantics>6.1</semantics> (<semantics>4.9</semantics>) percent. Furthermore, increasing the number of Halton draws either improves or has no detrimental impact on correlation, mean squared error, relative bias, and upward bias for persistent, transient, and total technical efficiency. An energy sector application is included, to demonstrate how these issues can arise in practice, and how increasing Halton draws can improve parameter and efficiency estimates in empirical work.