Diagnostic procedures for finding outliers in high dimensional multivariate time series and
robust estimation methods for these data are reviewed. First, methods for searching for …

Dynamic factor models have been developed out of the need of analyzing and forecasting
time series in increasingly high dimensions. While mathematical statisticians faced with …

The use of large-dimensional factor models in forecasting has received much attention in the
literature with the consensus being that improvements on forecasts can be achieved when …

General dynamic factor models have demonstrated their capacity to circumvent the curse of
dimensionality in the analysis of high-dimensional time series and have been successfully …

We propose the construction of conditional growth densities under stressed factor scenarios
to assess the level of exposure of an economy to small probability but potentially …

We propose a new global risk index, Growth-in-Stress (GiS), that measures the expected fall
in a country's GDP as the global factors, which drive world growth, are subject to stressful …

We study the problem of factor modelling vector-and tensor-valued time series in the
presence of heavy tails in the data, which produce anomalous observations with non …

Large and unexpected moves in the factors underlying economic growth should be the main
concern of policy makers aiming to strengthen the resilience of the economies. We propose …

We use static and dynamic factor models to decompose Greek inflation into common
components. Static factor analysis suggests the need to develop comprehensive underlying …

The use of large-dimensional factor models in forecasting has received much attention in the
literature with the consensus being that improvements on forecasts can be achieved when …