Patton and Timmermann (2012, 'Forecast Rationality Tests Based on Multi-Horizon Bounds', <I>Journal of Business & Economic Statistics</I>, 30(1) 1-17) propose a set of useful tests for forecast rationality or optimality under squared error loss, including an easily implemented test based on a...</i>

Patton and Timmermann (2011, 'Forecast Rationality Tests Based on Multi-Horizon Bounds', <I>Journal of Business & Economic Statistics</I>, forthcoming) propose a set of useful tests for forecast rationality or optimality under squared error loss, including an easily implemented test based on a...</i>

This discussion paper resulted in a publication IN the <a HREF="http://people.few.eur.nl/hkvandijk/PDF/Koop_and_Van_Dijk_2000_JoE_testing_for_integration.pdf">'Journal of Econometrics'</a>, 2000, 97(2), 261-291.<p> In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the...</p>

Accurate prediction of risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) requires precise estimation of the tail of the predictive distribution. Two novel concepts are introduced that offer a specific focus on this part of the predictive density: the censored posterior, a...

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model...