We develop a non-linear forecast combination rule based on copulas that incorporate the dynamic interaction between individual predictors. This approach is optimal in the sense that the resulting combined forecast produces the highest discriminatory power as measured by the receiver operating...

This paper discusses and documents the algorithms of SsfPack 2.2. SsfPack is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in state space form. The emphasis is on documenting the link we have made to the Ox computing...

Likelihood based estimation of the parameters of state space models can be carried out via a particle filter.  In this paper we show how to make valid inference on such parameters when the model is incorrect.  In particular we develop a simulation strategy for computing sandwich covariance...

This paper asks how well a general equilibrium agency cost model describes the dynamic relationship between credit variables and the business cycle. A Bayesian VAR is used to obtain probability intervals for empirical correlations. The agency cost model is found to predict the leading,...

In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by...

This paper is concerned with Markov chain Monte Carlo based Bayesian inference in generalized models of stochastic volatility defined by heavy-tailed student-t distributions (with unknown degrees of freedom) and covariate effects in the observation and volatility equations. A simple, fast and...

This paper is concerned with the Bayesian estimation of non-linear stochastic differential equations when only discrete observations are available. The estimation is carried out using a tuned MCMC method, in particular a blocked Metropolis-Hastings algorithm, by introducing auxiliary points and...

This paper is concerned with the fitting and comparison of high dimensional multivariate time series models with time varying correlations. The models considered here combine features of the classical factor model with those of the univariate stochastic volatility model. Specifically, a set of...

This paper is concerned with the estimation of stochastic differential equations when only discrete observations are available. It primarily focuses on deriving a closed form solution for the one-step ahead conditional transition density using the Milstein scheme. This higher order Taylor...