Tsay summarizes the basic concepts and ideas of analyzing multivariate dependent data, provides econometric and statistical models useful for describing the dynamic dependence between variables, discusses the identifiability problem when the models become too flexible, introduces ways to search for simplifying structure hidden in high-dimensional time series, addresses the applicabilities and limitations of multivariate time series
method, and develops a software package for readers to apply the methods and models he discusses.
Korobilis, 2009, "Bayesian Multivariate Time Series
Methods for Empi-cal Macroeconomics," MPRA Paper, No.
A sampling of topics: stability conditions in contextual emergence, contextual emergence of mental states from neurodynamics, mutual phase synchronization in single trial data, ordinal analysis of EEG time series, quantification of order patterns recurrence plots of event related potentials, testing for coupling asymmetry using surrogate data, measuring the thalamocortical loop in patients, and assessment of connectivity patterns from multivariate time series
by partial directed coherence.
Performance of alerts generated by individual monitoring of aggregate data and separate data streams, and simultaneous monitoring of multiple data streams by using univariate and multivariate time series
models, Hong Kong, 1998-2007 * Univariate models Sensitivity Data AUWROC ([double dagger]) Aggregated data GP 0.
The third chapter extends the discussion to multivariate time series
models represented by vector-autoregressive (VAR) and vector-autoregressive moving average (VARMA) models.