A class of model-based filters for extracting trends and cycles in economic time series is presented. These lowpass and bandpass filters are derived in a mutually consistent manner as the joint solution to a signal extraction problem in an unobserved-components model. The resulting trends and...

This article develops a new method for detrending time series. It is shown how, in a Bayesian framework, a generalized version of the Hodrick-Prescott filter is obtained by specifying prior densities on the signal-to-noise ratio (q) in the underlying unobserved components model. This helps...

Oil prices clearly play an important role in the macroeconomy. The dynamics of oil prices have, however, been difficult to pin down because of the frequent occurrence of large shocks. In this paper, we propose a time series model with heavy-tailed disturbances to analyze the dynamics of the oil...

This article discusses the discretization of continuous-time filters for application to discrete time series sampled at any fixed frequency. In this approach, the filter is first set up directly in continuous-time; since the filter is expressed over a continuous range of lags, we also refer to...