We propose two fast, stable and consistent methods to estimate time series models expressed in their equivalent state-space form. They are useful both, to obtain adequate initial conditions for a maximum-likelihood iteration, or to provide final estimates when maximum-likelihood is considered...

We propose a new procedure to detect unit roots based on subspace methods. It has three main original features. First, the same method can be applied to single or multiple time series. Second, it employs a flexible family of information criteria, which loss functions can be adapted to the...

Computing the gaussian likelihood for a nonstationary state-space model is a difficult problem which has been tackled by the literature using two main strategies: data transformation and diffuse likelihood. The data transformation approach is cumbersome, as it requires nonstandard filtering. On...

This paper shows that price level trends in many of the EMU countries evolve with different patterns and that these patterns will not converge in the long-run. We propose that the hypothesis of price convergence should be evaluated and tested employing the relative prices. To this aim, we: (i)...

In this paper we propose a new method to specify linear models for vectors of time series with some convenient properties: First, it provides a unique modeling approach for single and multiple time series, as the same decisions are required in both cases. Second, it is scalable, meaning that it...

This paper discusses how to specify the order of a state-space model. To do so, we start by revising existing approaches and find in them two basic shortcomings: (i) some of them have a poor performance in short samples and (ii) most of them are not robust, meaning that their performance...