a b s t r a c t
The BeveridgeNelson (BN) decomposition is a model-based method for decomposing time series
into permanent and transitory components. When constructed from an ARIMA model, it is closely
related to decompositions based on unobserved components (UC) models with random walk trends and
covariance stationary cycles. The decomposition when extended to I.2/ models can also be related to
non-model-based signal extraction filters such as the HP filter. We show that the BN decomposition
provides information on the correlation between the permanent and transitory shocks in a certain class
of UC models. The correlation between components is known to determine the smoothed estimates
of components from UC models. The BN decomposition can also be used to evaluate the efficacy of
alternative methods. We also demonstrate, contrary to popular belief, that the BN decomposition can
produce smooth cycles if the reduced form forecasting model is appropriately specified.
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