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...

A dynamic random effects probit model is estimated on the first six waves of the German Socio-Economic Panel to test for state dependence effects in male unemployment behaviour. Estimation of the model is based on the marginal likelihood approach. In the model an individual's unemployment...

A dynamic random effects probit model is estimated on the first six waves of the German Socio-Economic Panel to test for state dependence effects in male unemployment behaviour. Estimation of the model is based on the marginal likelihood approach. In the model an individual's unemployment...

In this paper we use the Hodrick-Prescott filter for analyzing 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 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 λ = 1600 as something like an universal constant. It is well known that the HP filter is an optimal filter under some...