The HP filter is the most popular filter for extracting the trend and cycle components from an observed time series. Many researchers consider the smoothing parameter e͏̈ = 1600 as something like an universal constant. It is well known that the HP filter is an optimal filter under some...

This paper proposes a novel regularisation method for the estimation of large covariance matrices, which makes use of insights from the multiple testing literature. The method tests the statistical significance of individual pair-wise correlations and sets to zero those elements that are not...

In this paper we use the Hodrick-Prescott filter for analysing global temperature data. We are especially concerned with a reliable estimation of the trend component at the end of the data sample. To this end we employ time-varying values for the penalization parameter. The optimal values are...

Quarterly GDP figures usually are published with a delay of some weeks. A common way to generate GDP series of higher frequency, i.e. to nowcast GDP, is to use available indicators to calculate a single index by means of a common factor derived from a dynamic factor model (DFM). This paper deals...

This paper proposes a very general time series framework to capture the long-run behaviour of financial series. The suggested model includes linear and non-linear time trends, and stationary and nonstationary processes based on integer and/or fractional degrees of differentiation. Moreover, the...

This paper uses a seasonal long-memory model to capture the behaviour of the US Industrial Production Index (IPI) over the period 1919Q1-2022Q4. This series is found to display a large value of the periodogram at the zero, long-run frequency, and to exhibit an order of integration around 1. When...