This thesis focusses on econometric applications requiring multivariate numerical integration. Models that attempt to capture real world complexities are typically nonlinear and display many unobservable factors. These characteristics imply that the likelihood function of these models contain...

We develop a numerical filtering procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-gaussian state-space models. The procedure approximates necessary integrals using continuous or piecewise-continuous approximations of target densities....