Ways of improving the efficiency of Monte-Carlo (MC) techniques are studied for dynamic models. Such models cause the conventinal Antithetic Variate (AV) technique to fail, and will be proved to reduce the benefit from using Control Variates with nearly non-stationary series. This paper suggest modifications of the two conventional variance reduction techniques to enhance their efficiency. New classes of AV's are also proposed. Methods of reordering residuals are found to do less well than others wich rely on changing signs in the spirit of the traditional AV. Then, unconventional applications of these techniques of MC work for nearly nonstationary series. It generates econometric estimators at one and a half times the speed of conventional MC.
