Fixed coecients State-Space and VARMAX models are equivalent, meaning that they are able to represent the same linear dynamics, being indistinguishable in terms of overall fit. However, each representation can be specifically adequate for certain uses, so it is relevant to be able to choose...

En este trabajo se propone un nuevo procedimiento para detectar ra´ıces unitarias basado en m´etodos de subespacios. Nuestra propuesta tiene tres aspectos originales principales. Primero, la misma metodología puede aplicarse a series individuales o a vectores de series temporales. Segundo,...

This paper shows how to compute the in-sample effect of exogenous inputs on the endogenous variables in any linear model written in state-space form. Estimating this component may be, either interesting by itself, or a previous step before decomposing a time series into trend, cycle, seasonal...

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)...

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...

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...

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...