This discussion paper resulted in an article in the <I>Journal of the Royal Statistical Society Series A</I> (2008). Vol. 171, issue 1, pages 265-277.<P> Risk is at the center of many policy decisions in companies, governments and other institutions. The risk of road fatalities concerns local governments...</p></i>

This discussion paper led to a publication in the 'Journal of Applied Econometrics'</I>, 2014, 29(1), 65-90.<P> We propose a new approach to the modelling of the term structure of interest rates. We consider the general dynamic factor model and show how to impose smoothness restrictions on the factor...</p>

We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...

This discussion paper resulted in an article in the <I>International Journal of Forecasting</I> (2014). Volume 30, pages 572-584.<P> We explore a new approach to the forecasting of macroeconomic variables based on a dynamic factor state space analysis. Key economic variables are modeled jointly with...</p></i>

Many empirical studies have shown that factor models produce relatively accurate forecasts compared to alternative short-term forecasting models. These empirical findings have been established for different macroeconomic data sets and different forecast horizons. However, various specifications...

This discussion paper has resulted in a publication in the <I>Journal of Business and Economic Statistics<I>.<p>In this discussion paper we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different...</p></i></i>

An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear log-density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian...

This discussion paper led to an article in <I>Applied Economics</I> (2013). Vol. 45, pages 3024-3034.<P> The basic structural time series model has been designed for the modelling and forecasting of seasonal economic time series. In this paper we explore a generalisation of the basic structural time...</p></i>

We consider likelihood inference and state estimation by means of importance sampling for state space models with a nonlinear non-Gaussian observation y ~ p(y|alpha) and a linear Gaussian state alpha ~ p(alpha). The importance density is chosen to be the Laplace approximation of the smoothing...

This discussion paper led to an article in the <I>Journal of Time Series Analysis</I> (2010). Vol. 31, pages 407-414.<P> State space models with nonstationary processes and fixed regression effects require a state vector with diffuse initial conditions. Different likelihood functions can be adopted for...</p></i>