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Abstract:
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The classical vector autoregressive (VAR) models have been widely used to model multivariate time series data, because of their flexibility and ease of use. However, the VAR model suffers from overparameterization particularly when the number of lags and number of time series get large. There are several statistical methods of achieving dimension reduction of the parameter space in VAR models, however, these methods are inefficient to extract relevant information from a complex body of data because they fail to distinguish between information that is useful to the scientific goals. Envelope methods are based on novel parameterizations that use reducing subspaces to link between the mean function and dispersion matrix and are able to identify and remove the irrelevant information. In this talk, we propose a new parsimonious VAR model by incorporating the idea of envelope models into the reduced-rank VAR model that can achieve substantial dimension reduction and efficient parameter estimation. The results of simulation studies and real data analysis that compare the performance of the proposed model with that of the existing models in the literature will be presented.
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