The basic structural time series model has been designed for the modelling and forecasting of seasonal economic time series. In this paper we explore a generalisation of the basic structural time series model in which the time-varying trigonometric terms associated with different seasonal...

We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...

Building on a model of the interaction of risk-averse frms that compete in forward and spot markets, we develop an empirical strategy to test whether oligopolistic frms use forward contracts for strategic motives, for risk-hedging, or for both. An increase in the number of players weakens the...

This paper analyzes the possibilities to relieve congestion using rewards instead of taxes, as well as combinations of rewards and taxes. The model considers a Vickrey-ADL model of bottleneck congestion with endogenous scheduling. With inelastic demand, a fine (time-varying) reward is equivalent...

Forecasts from various experts are often used in macroeconomic forecasting models. Usually the focus is on the mean or median of the survey data. In the present study we adopt a different perspective on the survey data as we examine the predictive power of disagreement amongst forecasters. The...

Conventional hedonic analysis measures willingness to pay for attributes on the basis of marginal fixed costs. We argue that in many cases variable costs are also affected by these attributes and that this should be taken into account. We develop a simple model to show that the marginal...

This paper has been accepted for publication in the 'Review of Economics and Statistics'.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...

We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that...