  Stochastic variational inequalities (SVI) provide a means for modeling
various optimization and equilibrium problems where data are subject to
uncertainty. Often it is necessary to estimate the true SVI solution by the
solution of a sample average approximation (SAA) problem. This paper proposes
three methods for building confidence intervals for components of the true
solution, and those intervals are computable from a single SAA solution. The
first two methods use an "indirect approach" that requires initially computing
asymptotically exact confidence intervals for the solution to the normal map
formulation of the SVI. The third method directly constructs confidence
intervals for the true SVI solution; intervals produced with this method meet a
minimum specified level of confidence in the same situations for which the
first two methods are applicable. We justify the three methods theoretically
with weak convergence results, discuss how to implement these methods, and test
their performance using two numerical examples.
