The extremal index (O) is the key parameter for extending extreme value theory results from i.i.d. to stationary sequences. One important property of this parameter is that its inverse determines the degree of clustering in the extremes. This article introduces a novel interpretation of the...

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to...

This paper proposes a functional coefficient quantile regression model with heterogeneous and time-varying regression coefficients and factor loadings. Estimation of the model coefficients is done in two stages. First, we estimate the unobserved common factors from a linear factor model with...

This paper presents a novel test of cointegration that is robust to general forms of weak dependence in the innovation sequences and is simple to implement. In contrast to existing procedures, this is achieved without applying corrections to the test statistic for removing the effect of serial...

Grounded on the concept of cointegration, this paper develops a novel test of time series convergence between pairs of unit root processes. The test (i) does not require the estimation of the cointegration coeffcient, (ii) is robust to general forms of weak dependence in the transitory...