Publication: Discovering general and sectorial trends in a large set of time series
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2020-09-29
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Abstract
The objective of this research note is to extend the pairwise procedure studied by Car-
lomagno and Espasa (ming) to the case of general and sectorial trends. The extension
allows to discover subsets of series that share general and/or sectorial stochastic trends
between a (possible large) set of time series. This could be useful to model and forecast
all of the series under analysis. Our approach does not need to assume pervasiveness of
the trends, nor impose special restrictions on the serial or cross-sectional idiosyncratic
correlation of the series. Additionally, the asymptotic theory works both, with finite
N and T ! 1, and with [T;N] ! 1. In a Monte Carlo experiment we show that the
extended procedure can produce reliable results in finite samples.
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Cointegration, Factor Models, Disaggregation, Pairwise Tests, Heteroskedasticity