  Quasi-Monte Carlo (qMC) methods are a powerful alternative to classical
Monte-Carlo (MC) integration. Under certain conditions, they can approximate
the desired integral at a faster rate than the usual Central Limit Theorem,
resulting in more accurate estimates. This paper explores these methods in a
simulation-based estimation setting with an emphasis on the scramble of Owen
(1995). For cross-sections and short-panels, the resulting Scrambled Method of
Moments simply replaces the random number generator with the scramble
(available in most softwares) to reduce simulation noise. Scrambled Indirect
Inference estimation is also considered. For time series, qMC may not apply
directly because of a curse of dimensionality on the time dimension. A simple
algorithm and a class of moments which circumvent this issue are described.
Asymptotic results are given for each algorithm. Monte-Carlo examples
illustrate these results in finite samples, including an income process with
"lots of heterogeneity."
