A Direct Monte Carlo (DMC) approach is introduced for posterior simulation in theInstrumental Variables (IV) model with one possibly endogenous regressor, multipleinstruments and Gaussian errors under a flat prior. This DMC method can also beapplied in an IV model (with one or multiple...

Combined forecasts from a linear and a nonlinear model areinvestigated for timeseries with possibly nonlinear characteristics. The forecasts arecombined by aconstant coefficient regression method as well as a time varyingmethod. Thetime varying method allows for a locally (non)linear model....

Adaptive Polar Sampling is proposed as an algorithm where random drawings aredirectly generated from the target function (posterior) in all-but-onedirections of the parameter space. The method is based on the mixed integrationtechnique of Van Dijk, Kloek & Boender (1985) but extends this one by...

Exchange rates typically exhibit time-varying patterns in both means andvariances. The histograms of such series indicate heavy tails. In thispaper we construct models which enable a decision-maker to analyze theimplications of such time series patterns for currency risk management.Our approach...

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 autoregressive representation or in a separate state equation. Tests based on the former are analogous to...

Diffuse priors lead to pathological posterior behavior when used in Bayesian analyses of Simultaneous Equation Models (SEMs). This results from the local nonidentification of certain parameters in SEMs. When this, a priori known, feature is not captured appropriately, it results in an a...

A major problem in applying neural networks is specifying the sizeof the network. Even for moderately sized networks the number ofparameters may become large compared to the number of data. In thispaper network performance is examined while reducing the size of thenetwork through the use of...

Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlomethod for Bayesian analysis of models with ill-behaved posteriordistributions. In order to sample efficiently from such a distribution,a location-scale transformation and a transformation to polarcoordinates are used. After...

In this paper, we make use of state space models toinvestigate the presence of stochastic trends in economic time series. Amodel is specified where such a trend can enter either in the autoregressiverepresentation or in a separate state equation. Tests based on the formerare analogous to...

The failure to describe the time series behaviour of most realexchange rates as temporary deviations from fixedlong-term means may be due to time variation of the equilibriathemselves, see Engel (2000). We implement thisidea using an unobserved components model and decompose theobservations on...