In this paper, I examine the properties of the class of generalized empirical likelihood estimators of moment-condition models. These nonparametric likelihood estimators satisfy exactly the moment conditions and automatically remove any bias due to a lack of centering. Moreover, the bias of the...

We propose a new semiparametric procedure for estimating multivariate models with conditioning variables. The semiparametric model is based on the parametric conditional copula and nonparametric conditional marginals. To avoid the curse of dimensionality in the estimation of the latter, we...

This paper considers two models to deal with an outcome variable that contains a large fraction of zeros, such as individual expenditures on health care: a sample-selection model and a two-part model. The sample-selection model uses two possibly correlated processes to determine the outcome: a...

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...

This paper examines evidence of long- and short-run co-movement in Canadian sectoral output data. Our framework builds on a vector-error-correction representation that allows to test for and compute full-information maximum-likelihood estimates of models with codependent cycle restrictions. We...

The performance of Monte Carlo integration methods like importance-sampling or Markov-Chain Monte-Carlo procedures depends greatly on the choice of the importance- or candidate-density. Such a density must typically be "close" to the target density to yield numerically accurate results with...

We develop a new approach to valuing and hedging basket options. We consider baskets of assets with potentially negative portfolio weights (spread options are a subclass of such basket options). The basket distribution is approximated using a generalized family of log-normal distributions. This...

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...