This paper presents exact recursions for calculating the mean and mean square error matrix of the state vector given the observations for the multi-variate linear Gaussian state-space model in the case where the initial state vector is (partially) diffuse. Copyright 2003 Blackwell Publishing Ltd.

We model 1927-1997 U.S. business failure rates using a time series approach based on unobserved components. Clear evidence is found of cyclical behavior in default rates. The cycle has a period of around 10 years. We also detect longer term movements in default probabilities and default...

Diagnostic checking of time-series models is normally carried out using the innovations, that is the one-step ahead prediction errors. In an unobserved components model, other sets of residuals are available. These auxiliary residuals are estimators of the disturbances associated.with the...

In this paper we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First we consider unobserved...

This paper discusses computationally efficient methods for exact maximum-likelihood estimation of parameters in state-space models. The proposed strategy is based on direct maximisation of the likelihood function, and it can be applied to a wide range of practical univariate and multivariate...

We propose an observation-driven dynamic factor model for mixed-measurement and mixed-frequency panel data. Time series observations may come from a range of families of distributions, be observed at different frequencies, have missing observations, and exhibit common dynamics and...