Estimation of dynamic models using kernel density

Abstract

The objective of this work is to present a simple approach to deal with a dynamic model without imposing any assumptions on the error distribution. Using a state-space representation the model does not need any optimization procedure to estimate the system parameters because it is optimized during an iterative process of prediction and filtering. The kernel approach obviates the need to estimate the usual unknown paramenters related to error densities.