Analysing steady-state simulation output using vector autoregressive processes with exogenous variables

A simulation study often requires computation of a point estimate and confidence region for the steady-state mean of a stochastic output process. The literature offers a variety of statistical techniques, including replication/deletion, the batch-means method, and spectrum analysis. We present a new multivariate output-analysis technique that is based on the general autoregressive time-series model with exogenous variables to set up a joint confidence region for the steady-state mean. We demonstrate our technique by an extensive computational experiment, and show that it performs at least as well as other output-analysis techniques, without having some of their drawbacks.