Figure 12 - available via license: Creative Commons Attribution 4.0 International
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Poles (x) and zeros (o) of the transfer functions corresponding to the three models: Mod 1 (red), Mod 2 (blue), Mod 3 (magenta).
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This paper investigates the asymptotic properties of estimators obtained from the so called CVA (canonical variate analysis) subspace algorithm proposed by Larimore (1983) in the case when the data is generated using a minimal state space system containing unit roots at the seasonal frequencies such that the yearly difference is a stationary vector...
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The paper is focused on comparing the forecasting performance of two relatively new types of Vector Error Correction - Multiplicative Stochastic Factor (VEC-MSF) specifications: VEC-MSF with constant conditional correlations, and VEC-MSF-SBEKK with time-varying conditional correlations. For the sake of comparison, random walks, vector autoregressio...
Citations
... Finally, as mentioned above, we do not address the case of seasonal cointegration. Recently, Bauer and Buschmeier (2021) went further in this sense proving that CVA, see Sect. 2.3, provides consistent estimators for long-run and short-run dynamics, even in the presence of seasonal unit roots. ...
We propose a new method to specify linear models for vectors of time series with some convenient properties. First, it provides a unified modeling approach for single and multiple time series, as the same decisions are required in both cases. Second, it is scalable, meaning that it provides a quick preliminary model, which can be refined in subsequent modeling phases if required. Third, it is optionally automatic, because the specification depends on a few key parameters which can be determined either automatically or by human decision. And last, it is parsimonious, as it allows one to choose and impose a canonical structure by a novel estimation procedure. Several examples with simulated and real data illustrate its application in practice.
... They propose and investigate a frequency-specific spectral ratio statistic, which is used to uncover differences in the spread of spectral information in a pair of such time series, and which is applied to data from stroke experiments. Another article on multivariate time series, following types of integrated vector ARMA models, is the one by Bauer and Buschmeier [8], who investigate estimators resulting from canonical variate analysis as well as novel cointegration tests. For illustration, they present an application to hourly electricity consumption data. ...
Time series consist of data observed sequentially in time, and they are assumed to stem from an underlying stochastic process [...]
