We introduce a methodology to characterise financial cycles combining a novel multivariate spectral approach to identifying common cycle frequencies across a set of indicators, and a time varying aggregation emphasising systemic developments. The methodology is applied to 13 European Union...

Long-term forecasts are of key importance for the car industry due to the lengthy period of time required for the development and production processes. With this in mind, this paper proposes new multivariate models to forecast monthly car sales data using economic variables and Google online...

Almost all existing nonlinear multivariate time series models remain linear, conditional on a point in time or latent regime. Here, an alternative is proposed, where nonlinear serial and cross-sectional dependence is captured by a copula model. The copula defines a multivariate time series on...

Potential output—in the sense of the GDP level or path an economy can sustain over the medium term—is a crucial benchmark for policymakers. However, it is difficult to estimate when financial “booms and busts” are driving the real economy. This paper uses a simple multivariate filtering...

In the Knowledge-Based Economy R&D activity of companies seemed to be crucial. The paper presents the possibility of using zero unitharisation method to identify those EU companies which are characterized by the highest levels of multidimensional R&D position. As a result there was created...

For the Markov property of a multivariate process, a necessary and sufficient condition on the multi-dimensional copula of the finite-dimensional distributions is given. This establishes that the Markov property is solely a property of the copula, i.e., of the dependence structure. This extends...

Trend models are important in describing nonstationary behavior of a time series. In this paper we propose valid tests for the trend coefficients in a multivariate system with mixed stationary, integrated or nearly integrated errors. Cross-sectional and serial dependence in innovations are left...

The causative agents of social and economic inequalities may be multivariate ranging from natural to man-made factors which have varying degrees of susceptibilities to reduction. Efforts of their dilution entail strenuous efforts starting from an understanding of the causative factors, to...

Modeling extreme events is of paramount importance in various areas of science — biostatistics, climatology, finance, geology, and telecommunications, to name a few. Most of these application areas involve multivariate data. Estimation of the extreme value index plays a crucial role in...

We study a group lasso estimator for the multivariate linear regression model that accounts for correlated error terms. A block coordinate descent algorithm is used to compute this estimator. We perform a simulation study with categorical data and multivariate time series data, typical settings...