Outliers are observations that deviate significantly from the norm, and their detection has been a critical topic in various research areas and application domains, such as video surveillance, network intrusion detection, and disease outbreak detection. In recent years, deep learning-based...

In the monthly ifo Business Survey around 9,000 German companies answer questions about their current business situation, expectations and plans for the near future as well as on other business variables. This paper provides an overview of all regular questions (monthly, quarterly, bi-annually,...

The main goal of the article is to investigate forecasting quality of two approaches to modelling main macroeconomic variables without a priori assumptions concerning causality and generate forecasts without additional assumptions regarding regressors. With application of tendency survey data...

The article compares forecast quality from two atheoretical models. Neither method assumed a priori causality and forecasts were generated without additional assumptions about regressors. Tendency survey data was used within the Bayesian averaging of classical estimates (BACE) framework and...

In this article we present four diversified approaches to forecasting main macroeconomic variables without a priori assumptions concerning causality. We include tendency survey data in both the Bayesian averaging of classical estimates (BACE) and the dynamic factor models (DFM) frameworks. With...

Economic agents are aware to incur in a loss basing their decisions on their own extrapolations instead of sound statistical data, but the loss could be smaller than the one related to waiting for the dissemination of final data. A broad guidance in deciding when statistical offices should...

The approach by Engelberg, Manski, and Williams (2009) to convert probabilistic survey responses into continuous probability distributions implicitly assumes that the question intervals are equally wide. Almost all recently established household surveys have intervals of varying widths. Applying...

We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...

This paper addresses the open debate about the effectiveness and practical relevance of highfrequency (HF) data in portfolio allocation. Our results demonstrate that when used with proper econometric models, HF data offers gains over daily data and more importantly these gains are maintained...

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a...