We propose a simple methodology for decomposing differences in the first moment into characteristics and coefficients effects. This methodology provides a way to apply the Blinder-Oaxaca decomposition to a non-linear function for both aggregate and detailed decompositions

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...

The aim of this paper is to study the concept of separability in multiple nonstationary time series displaying both common stochastic trends and common stochastic cycles. When modeling the dynamics of multiple time series for a panel of several entities such as countries, sectors, firms,...

This paper deals with the estimation of the output gap. We use uni- and bivariate unobserved components models in order to decompose the observed German GDP-series into trend, cycle and seasonal components. The results show that using the ifo business assessment variable as an indicator for the...

This paper considers a partitioned population and develops a decomposition of the Gini index in two components, which measure the within and the between groups inequality. Differently from the most widespread inequality measure decompositions, having a between component that compares the means...

In this paper, we examine if there are gender differences in total bankcard limits by utilizing a data set that links mortgage applicant information with individual-level credit bureau data from 2006 to 2016. We document that after controlling for credit score, income, and demographic...

Data security is considered a significant issue while doing communication over cyberspace, as the quantity of information shared through the virtual world is expanding exponentially day by day. Steganography can play an integral part in safe-guarding the data from the unauthorized users through...