Abstract
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 (first step) and principal components analysis (second step). Differently from other available methods, it allows the modelling of large data sets, both in terms of temporal and cross-sectional dimensions. Monte Carlo evidence, supporting the two-step estimation procedure, is also provided, as well as an empirical application to real data.
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