We propose a new class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled score of the likelihood function. This approach provides a unified and consistent framework for introducing...

This discussion paper led to an article in <I>Statistica Neerlandica</I> (2003). Vol. 57, issue 4, pages 439-469.<P> The linear Gaussian state space model for which the common variance istreated as a stochastic time-varying variable is considered for themodelling of economic time series. The focus of this...</p></i>

This discussion paper resulted in an article in the <I>Journal of the American Statistical Association</I> (2007). Vol. 102, issue 477, pages 16-27.<p> Novel periodic extensions of dynamic long memory regression models with autoregressive conditional heteroskedastic errors are considered for the analysis...</p></i>

This discussion paper led to a publication in the <I>International Journal of Forecasting</I> (2013). Vol. 29, pages 676-694.<P> We extend the class of dynamic factor yield curve models for the inclusion of macro-economic factors. We benefit from recent developments in the dynamic factor literature for...</p></i>

Many seasonal macroeconomic time series are subject to changes in their means and variances over a long time horizon. In this paper we propose a general treatment for the modelling of time-varying features in economic time series. We show that time series models with mean and variance functions...

This paper resulted in a publication in the <I>Journal of Econometrics</I> (2014). Volume 180, pages 127-140.<P> An exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non-Gaussian dynamic panel data model with unobserved random individual-specific and time-varying...</p></i>

This discussion paper led to an article in the <I>Oxford Bulletin of Economics and Statistics</I> (2009). Vol. 71, pages 683-713.<P> This paper discusses identification, specification, estimation and forecasting for a general class of periodic unobserved components time series models with stochastic...</p></i>

This version has replaced the version of January 30, 2012.<P> A successful construction of an importance density for nonlinear non-Gaussian state space models is crucial when Monte Carlo simulation methods are used for likelihood evaluation, signal extraction of dynamic latent factors and...</p>

Accepted by the <Journal of Empirical Finance</I>.<P> We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to...</p></journal>

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>