This paper shows how a high level matrix programming language may be used to perform Monte Carlo simulation, bootstrapping, estimation by maximum likelihood and GMM, and kernel regression in parallel on symmetric multiprocessor computers or clusters of workstations. The implementation of...

The shape of the likelihood of several recently developed econometric models is often non-elliptical. Learning this shape using Gibbs sampling is discussed in this paper. A systematic analysis using graphical and computational methods is presented. Examples of the models considered in this paper...

This paper describes and analyses the use of the Filtered Historical Simulation algorithm in pricing spread options. Spread options are contracts whose payoff depends on the price difference (spread) between two or more underlying assets at a future date. Such kind of options are written in the...

Computing power now allows empirical researchers to use intensive computing estimation techniques with nonlinear panel-data models. Maximum Likelihood estimation is often cumbersome, if not analytically intractable, when dealing with such models. Even the simple calculation of the likelihood...

A simulation smoother in state space time series analysis is a procedure for drawing samples from the conditional distribution of state or disturbance vectors given the observations. We present a new technique for this which is both simple and computationally efficient.

This note describes ParallelKnoppix, a bootable CD that allows econometricians with average knowledge of computers to create and begin using a high performance computing cluster for parallel computing in very little time. The computers used may be heterogeneous machines, and clusters of up to...

Advances in computing power allow the empirical researcher to use intensive computional techniques to solve and estimate nonlinear panel-data models, specifically those arising from nonlinear panel data such as Probit and Tobit models. In these cases, maximum-likelihood estimation can be...