We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the … conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the … efficient estimates of the parameters using a monthly dataset of core inflation for which we consider different subsamples of …

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the … conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the … efficient estimates of the parameters using a monthly dataset of core inflation for which we consider different subsamples of …

In this paper we explain how the importance sampling technique can be generalized from simulating expectations to … success of the generalized importance sampling is illustrated by numerical examples in the context of Asian option pricing …

Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the … generic and easily implementable SMC approach known as Particle Efficient Importance Sampling (PEIS). By using SMC importance … sampling densities which are approximately fully globally adapted to the targeted density of the states, PEIS can substantially …

The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high … number of successful applications in high dimensions, it is well known that importance sampling strategies are subject to an … sampling squared algorithms …

This paper develops a systematic Markov Chain Monte Carlo (MCMC) framework based upon Efficient Importance Sampling … solution. EIS is a simple, generic and yet accurate Monte-Carlo integration procedure based on sampling densities which are … MCMC components such as auxiliary sampling densities, normalizing constants and starting values. The potential of this …