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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...
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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...
Persistent link: https://www.econbiz.de/10008520475
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,...
Persistent link: https://www.econbiz.de/10008520484
This paper discusses how to specify an observable high-frequency model for a vector of time series sampled at high and low frequencies. To this end we first study how aggregation over time affects both the dynamic components of a time series and their observability, in a multivariate linear...
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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...
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