DeJong, David Neil Dharmarajan, Hariharan Liesenfeld, Roman Richard, Jean-François
Year of Publication:
Economics Working Paper No. 2007-25
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. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information.
particle filter adaption efficient importance sampling kernel density approximation