This paper deals with estimating small tail probabilities of the steady-state waiting time in a GI/GI/1 queue with heavy-tailed (subexponential) service times. The problem of estimating infinite horizon ruin probabilities in insurance risk processes with heavy-tailed claims can be transformed...

The structural vector autoregression (SVAR) has become a central tool for research in empirical macroeconomics. Because the vast majority of these models are exactly identified, researchers have traditionally relied upon the informal use of prior information to compare alternative...

This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov Chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the...

In this paper we estimate tail probabilities for the sum of Lognormal distributions. We propose to use a defensive mixture, and develop a method of finding the optimal density via the EM algorithm; we also consider the technique which assumes the importance sampling density to belong to the same...

An extensive literature in econometrics and in numerical analysis has considered the problem of evaluating the multiple integral P({bold B};mu,Omega) = integral_{a}^{b} n(v - mu, Omega)dv = E_{V}{bold 1}(V in {bold B}), where V is a m-dimensional normal vector with mean mu, covariance matrix ,...

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 conditional variance is modelled by a stochastic...

We estimate stochastic volatility leverage models for a panel of stock returns for 24 S&P 500 firms from six industries. News are measured as differences between daily return and a monthly moving average of past returns. We estimate the models by maximum likelihood using an Efficient Importance...

<Para ID="Par1">We formulate an objective as a convex combination of expectation and risk, measured by the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\mathrm{CVaR }$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi mathvariant="normal">CVaR</mi> </math> </EquationSource> </InlineEquation> risk measure. The poor performance of standard Monte Carlo estimators applied on functions of this form is discussed and a variance reduction scheme based on importance...</equationsource></equationsource></inlineequation></para>

The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian...

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...