DSGE Model Estimation on the Basis of Second-Order Approximation

This article compares the properties of different non-linear Kalman filters: the well-known Unscented Kalman filter (UKF), the central difference Kalman filter (CDKF) and the new Quadratic Kalman filter (QKF). A small financial DSGE model is repeatedly estimated by several quasi-likelihood methods with different filters for data generated by the model. Errors in parameters estimation are a measure of the filters’ quality. The result shows that the QKF has a reasonable advantage in terms of quality over the CDKF and the UKF, albeit with some loss in speed. Copyright Springer Science+Business Media New York 2014