This paper suggests solutions to two different types of simulation errors related to Quasi-Monte Carlo integration. Likelihood functions which depend on standard deviations of mixed parameters are symmetric in nature. This paper shows that antithetic draws preserve this symmetry and thereby improves precision substantially. Another source of error is that models testing away mixing dimensions must replicate the relevant dimensions of the quasi-random draws in the simulation of the restricted likelihood. These simulation errors are ignored in the standard estimation procedures used today and this paper shows that the result may be substantial estimation- and inference errors within the span of draws typically applied.
Quasi-Monte Carlo integration Antithetic draws Likelihood Ratio tests simulated likelihood panel mixed multinomial logit Halton draws