This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for...

Due to their well-known indeterminacies, factor models require identifying assumptions to guarantee unique parameter estimates. For Bayesian estimation, these identifying assumptions are usually implemented by imposing constraints on certain model parameters. This strategy, however, may result...

This article evaluates the performance of structural equation models in validating measurement models for hypothetical constructs and deals with specific issues following from the way this methodology is typically applied in scale construction. In particular, controlling for various types of...

Due to their well-known indeterminacies, factor models require identifying assumptions to guarantee unique parameter estimates. For Bayesian estimation, these identifying assumptions are usually implemented by imposing constraints on certain model parameters. This strategy, however, may result...

In practical applications of micro simulation models very little is usually known about the properties of the simulated values. This paper argues that we need to apply the same rigorous standards for inference in micro simulation work as in scientific work generally. If not, then micro...

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to...

We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically...

The primary aim of the paper is to consider the problems and issues raised when the data exhibit time heterogeneity in the context of linear models. Ignoring time heterogeneity will undermine the reliability of inference and will give rise to untrustworthy evidence. Accounting for it using trend...