Variance Reduction Techniques in Monte Carlo Methods
CentER Discussion Paper Series No. 2010-117
18 Pages Posted: 26 Nov 2010 Last revised: 5 Dec 2010
Date Written: November 2, 2010
Abstract
Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the introduction of computers. This increased computer power has stimulated simulation analysts to develop ever more realistic models, so that the net result has not been faster execution of simulation experiments; e.g., some modern simulation models need hours or days for a single ’run’ (one replication of one scenario or combination of simulation input values). Moreover there are some simulation models that represent rare events which have extremely small probabilities of occurrence), so even modern computer would take ’for ever’ (centuries) to execute a single run.were it not that special VRT can reduce theses excessively long runtimes to practical magnitudes.
Keywords: common random numbers, antithetic random numbers, importance sampling,control variates, conditioning, stratified sampling, splitting, quasi Monte Carlo
JEL Classification: C0, C1, C9
Suggested Citation: Suggested Citation