Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

Obgleich es zahlreiche Institutionen gibt, die das deutsche Bruttoinlandsprodukt vorhersagen, mangelt es aktuell an sogenannten "Nowcasting"-Modellen, deren Prognosegüte mit jedem neu verfügbaren, das deutsche Bruttoinlandsprodukt beeinflussenden Datenpunkt verbessert wird. In der folgenden...

This paper introduces a new approach to modelling the conditional variance in a multivariate setting. It is essentially a combination of the popular GARCH model class with a spatial component, inspired by generalized space-time models. The resulting spatial GARCH model takes into account both...

Wir beschäftigen uns mit dem Auftreten von Autokorrelationseffekten bei der Messung langfristiger Markrpreisrisiken. Dazu diskutieren wir zwei verbreitete Methoden der Value at Risk (VaR) Schätzung, nämlich die Historische Simulation und die Wurzel-t-Regel. Beide Verfahren werden aufgrund...

Obgleich es zahlreiche Institutionen gibt, die das deutsche Bruttoinlandsprodukt vorhersagen, mangelt es aktuell an sogenannten "Nowcasting"-Modellen, deren Prognosegüte mit jedem neu verfügbaren, das deutsche Bruttoinlandsprodukt beeinflussenden Datenpunkt verbessert wird. In der folgenden...

This study aims to refine unemployment forecasts by incorporating the degree of consensus in consumers’ expectations. With this objective, we first model the unemployment rate in eight European countries using the step-wise algorithm proposed by Hyndman and Khandakar (J Stat Softw...

Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have...

Business tendency survey indicators are widely recognized as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified...