Efficient likelihood evaluation of state-space representations

We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure approximates necessary integrals using continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information. We illustrate our procedure in applications to dynamic stochastic general equilibrium models. -- particle filter ; adaption ; efficient importance sampling ; kernel density approximation ; dynamic stochastic general equilibrium model