Fractionally integrated models with the disturbances following a Bloomfield (1973) exponential spectral model are proposed in this article for modelling the U.K. unemployment. This enables us a better understanding of the low-frequency dynamics affecting the series, without relying on any...

We propose in this article a general time series model, whose components are modelled in terms of fractionally integrated processes. This specification allows us to consider the trend, the seasonal and the cyclical components as stochastic processes, including the unit root models as particular...

We make use in this article of a testing procedure suggested by Robinson (1994) for testing deterministic seasonality versus seasonal fractional integration. A new test statistic is developed to simultaneously test both, the order of integration of the seasonal component and the need of seasonal...

We propose in this article a joint test for testing simultaneously a deterministic trend component and the degree of integration of the cyclical component in a given time series. The test is directly derived from Robinson's (1994) procedure, which is based on the Lagrange Multiplier (LM)...

We show in this article that fractionally integrated univariate models for GDP may lead to a better replication of business cycle characteristics. We firstly show that the business cycle features are clearly affected by the degree of integration as well as by the other short run components of...

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...

We propose a new model for dynamic volatilities and correlations of skewed and heavy-tailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the...

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 dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete stock price changes. The likelihood function for our model is analytically intractable and requires Monte Carlo integration methods for its numerical evaluation. The proposed...