  This paper considers a simulation-based estimator for a general class of
Markovian processes and explores some strong consistency properties of the
estimator. The estimation problem is defined over a continuum of invariant
distributions indexed by a vector of parameters. A key step in the method of
proof is to show the uniform convergence (a.s.) of a family of sample
distributions over the domain of parameters. This uniform convergence holds
under mild continuity and monotonicity conditions on the dynamic process. The
estimator is applied to an asset pricing model with technology adoption. A
challenge for this model is to generate the observed high volatility of stock
markets along with the much lower volatility of other real economic aggregates.
