  Calculating a Monte Carlo standard error (MCSE) is an important step in the
statistical analysis of the simulation output obtained from a Markov chain
Monte Carlo experiment. An MCSE is usually based on an estimate of the variance
of the asymptotic normal distribution. We consider spectral and batch means
methods for estimating this variance. In particular, we establish conditions
which guarantee that these estimators are strongly consistent as the simulation
effort increases. In addition, for the batch means and overlapping batch means
methods we establish conditions ensuring consistency in the mean-square sense
which in turn allows us to calculate the optimal batch size up to a constant of
proportionality. Finally, we examine the empirical finite-sample properties of
spectral variance and batch means estimators and provide recommendations for
practitioners.
