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...

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...

The production index is an important indicator for assessing the cyclical state of the economy. Unfortunately, the monthly time series is contaminated by many noisy components like seasonal variations, calendar and vacation effects. Only part of those nuisance components are explicitly...

In this paper an Unobserved Components Model is employed to decompose U.S. real GDP into trend and cycle components. The main findings are that there exist three cycles with a period of about two, five and 13 years, respectively, and that the long-run development during the last 50 years can be...

In this paper, an Unobserved Components Model is employed to decompose German real GDP into the trend, cycle and seasonal components and the working day effect. The most important findings are: 1) The growth rate of potential output declined from 4.2 per cent in the sixties to 1.4 per cent at...

This paper documents a comparative application of algorithms to deal with the problem of missing values in higher frequency data sets. We refer to Swiss business tendency survey (BTS) data which are conducted in both monthly and quarterly frequency, where an information sub-set is collected at...