A widely used filter to extract a signal in a time series, in particular in the business cycle analysis, is the Hodrick-Prescott filter. The model that underlies the filter considers the data series as the sum of two unobserved component (signal and non signal) and a smoothing parameter which for quarterly series is set to a specified value. This paper proposes a generalization of the Hodrick-P...

On purpose to extract trend and cycle from a time series many competing techniques have been developed. The probably most prevalent is the Hodrick Prescott filter. However this filter suffers from diverse shortcomings, especially the subjective choice of its penalization parameter. To this point penalized splines within a mixed model framework offer the advantage of a data driven derivation of ...

This paper argues that in assessing the effects of tax rates on aggregate hours of market work, it is essential to explicitly consider how the government spends tax revenues. Different choices regarding government spending lead to different elasticities of hours of work with regard to tax rates. I illustrate the empirical importance of this point by addressing the issue of hours worked and tax ...

Christopher Prescott, Arja Karivieri, Peter Campbell, Kristian Göransson & Sebastiano Tusa (ed.). 2021. Trinacria: ‘an island outside time’. International archaeology in Sicily. Oxford: Oxbow; 978-1-78925-591-1 hardback £55. - Volume 95 Issue 383

The Hodrick-Prescott filter represents one of the most popular method for trendcycle extraction in macroeconomic time series. In this paper we provide a multivariate generalization of the Hodrick-Prescott filter, based on the seemingly unrelated time series approach. We first derive closed-form expressions linking the signal-noise matrix ratio to the parameters of the VARMA representation of th...