Deletion diagnostics are developed for structural time series models. These show the effect of the deletion of individual observations on residuals and on the estimates of regression parameters. The methods are extended to the transformation of time series through regression on a constructed...

A stochastic variance model may be estimated by quasi-maximum likelihood procedure by transforming to a linear state space form. The properties of observations corrected for heteroscedasticity can be derived. A model with explanatory variables can be handled by correcting the observations for...

Many series are subject to data irregularities such as missing values, outliers, structural breaks and irregular spacing. Data can also be messy, and hence difficult to handle by standard procedures, when they are intrinsically non-Gaussian or contain complicated periodic patterns because they...

A number of important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recording day changes from year to year. In addtion certain festivals, most notably...

Much of economic analysis presupposes that certain economic time series can be decomposed into trends and cycles. Structural time series models are explicitly set up in terms of such unobserved components. This paper sets up various multivariate structural time series models, shows how they can...

A unified framework is established for the study of the computation of the distribution function from the characteristic function. A new approach to the proof of Gurland's and Gil-Pelaez's univariate inversion theorem is suggested. A multivariate inversion theorem is then derived using this...

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 develop a panel data model with stochastic dynamic processes to empirically verify the possible existence of the European crime drop. This time-varying effect can be captured by the stochastic trend and can be interpreted as the "potential" European crime drop. Due to the flexibility of our...

This paper introduces a novel simulation-based filtering method for general state space models. It allows for the computation of time-varying conditional means, quantiles, and modes, but also for the prediction of latent variables in general. The method relies on generating artificial samples of...