In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

This article presents a robust augmented Kalman filter that extends the data – cleaning filter (Masreliez and Martin, 1977) to the general state space model featuring nonstationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based...

To simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural changes, we introduce a time-varying parameter mixed-frequency VAR. To keep our approach from becoming too complex, we implement time variation parsimoniously: only the intercepts and a common...

This article presents a new filter for state-space models based on Bellman's dynamic programming principle applied to the posterior mode. The proposed Bellman filter generalises the Kalman filter including its extended and iterated versions, while remaining equally inexpensive computationally....

Over the last decades, the estimation of the slack in the economy has become an essential piece of analysis for policymakers, both on the monetary policy and the fiscal policy front. Output gap estimation techniques have flourished accordingly, although there is no consensus on a best-performing...

Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has at- tracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The...

In this paper we present a method for calculating the entire hedge surface of a derivative who’s future underlying asset has been simulated by a market simulator for example with the Monte Carlo method. Our method is built from work on penalized filtering techniques and is applied on a grid of...

By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting...