Multivariate modelling of long memory processes with common components
In the paper a new approach to the modelling of common components in long memory processes is introduced. The approach is based on a two-step procedure relying on Fourier transform methods (firrst step) and principal components analysis (second step), which, differently from previous contributions to the literature, allows the modelling of large data sets, both in terms of temporal and cross-sectional dimensions. Monte Carlo evidence, supporting the two-step stimation procedure, is also provided, as well as an empirical application to real data.
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