the underlying statistical distributions, a variety of analyticalmethods and simulation-based methods are available. Aside … orhistorical and Monte Carlo simulation methods. Although these approaches to overall VaR estimation have receivedsubstantial … and incremental VaR in either a non-normal analytical setting or a MonteCarlo / historical simulation context.This paper …

Highly non-elliptical posterior distributions may occur in several econometric models, in particular, when the … indirect sampling methods in these models, one has to find a good candidate density. In a recent paper - Hoogerheide, Kaashoek … sophisticated neural network simulation techniques is explored. In all examples considered in this paper – a bimodal distribution of …

Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed … dynamic econometric models. This paper reviews, adapts and compares three different approaches for solving this problem. For …

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlomethod for Bayesian analysis of models with ill … the location and scale.Tested on a set of canonical models that feature nearnon-identifiability, strong correlation, and …

We consider likelihood inference and state estimation by means of importance sampling for state space models with a … and smoother and the simulation smoother which do not rely on a linear Gaussian observation equation. Furthermore, results … are presented that lead to a more effective implementation of importance sampling for state space models. An illustration …

their performance in simulation experiments. This leads to a list of eight methodologic aspirations. Against their … background we criticize aspects of many simulation studies that have been used in the past to compare competing estimators for … dynamic panel data models. To illustrate particular pitfalls some further Monte Carlo results are produced, obtained from a …