A reduced form multivariate quantile autoregressive model is developed to study heterogeneity in the effects of macroeconomic shocks. This framework is used for forecasting and for constructing quantile impulse response ...

In this paper, we develop a reduced form multivariate quantile model, using a directional quantile framework. The proposed model is the solution to a collection of directional quantile models for a fixed orthonormal basis, ...

The aim of this paper is to provide evidence on output convergence among the Mercosur countries and associates, using multivariate time-series tests. The methodology is based on a combination of tests and estimation ...

We introduce a nonparametric breakpoint detection method for the means and covariances of a multivariate discrete time stochastic process. Breakpoints are defined as left or right endpoints of maximal intervals of local ...

Phenology is a traditional science that investigates the periodic phenomena of plants and animals and their relations to environmental conditions. Typically plant phenological studies are based on observations made by ...

The financial market presents non-linearities for the behavior of stock returns for periods of high and low market. This article studies portfolios whose variance-covariance matrices are estimates using a multivariate model ...

We introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial networks (GAN). Different from the state of the art, which traditionally ...

Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value ...

We introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial learning (GAN). Different from the state of the art, which traditionally ...