In the microsimulation literature, it is still uncommon to test the statistical significance of results. In this paper … we argue that this situation is both undesirable and unnecessary. Provided the parameters used in the microsimulation are … exogenous, as is often the case in static microsimulation of the first-order effects of policy changes, simple statistical tests …

methodology is thus termed "ROM simulation''. We discuss certain classes of random orthogonal matrices and show how each class … produces samples with different characteristics. ROM simulation has applications to many problems that are resolved using … illustration, we apply ROM simulation to determine the value-at-risk of a stock portfolio …

We develop a simulation algorithm that generates multivariate samples with exact means, covariances, and multivariate … simulation of risk factors for the risk management of financial institutions. We use the Kollo measure of multivariate skewness …

Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their … parameters from empirical data. Most simulation models do not possess closed form expressions for their likelihood function …, requiring the use of simulation-based inference, such as simulated method of moments, indirect inference or approximate Bayesian …

representativeness from the point of view of microsimulation analysis. We find important discrepancies between the data weighted with … the point of view of the accuracy of microsimulation results and we show that using a combination of demographic … calibration targets with several economic status variables or tax identifiers from the microsimulation model substantially …