We develop a score-driven time-varying parameter model where no particular parametric error distribution needs to be specified. The proposed method relies on a versatile spline-based density, which produces a score function that follows a natural cubic spline. This flexible approach nests the...

We study the performance of two analytical methods and one simulation method for computing in-sample confidence bounds for time-varying parameters. These in-sample bounds are designed to reflect parameter uncertainty in the associated filter. They are applicable to the complete class of...

A new model for time-varying spatial dependencies is introduced. It forms an extension to the popular spatial lag model and can be estimated conveniently by maximum likelihood. The spatial dependence parameter is assumed to follow a generalized autoregressive score (GAS) process. The theoretical...

We study the performance of alternative methods for calculating in-sample confidence and out of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty only. The out-of-sample bands reflect both parameter uncertainty and innovation uncertainty. The...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

We propose a dynamic factor model for mixed-measurement and mixed-frequency panel data. In this framework time series observations may come from a range of families of parametric distributions, may be observed at different time frequencies, may have missing observations, and may exhibit common...