Recently, the availability of high-frequency financial data has opened new research directions for modeling the volatility of asset returns. In particular, building on the theory of quadratic variation, the high-frequency ...

Chapter 1 proposes simple and robust diagnostic tests for spatial dependence, specifically for spatial error autocorrelation and spatial lag dependence. The idea of our tests is to reformulate the testing problem such that ...

The contribution of this dissertation is threefold. First, econometric procedures to test for the occurence of asset price bubbles ex post and in real time are proposed. Real time monitoring procedures represent an additional ...

The standard panel data literature is moving from micro panels, where the cross-section dimension is large and the intertemporal sample size is small, to large panels, where both, the cross-section and the time dimension, are large. This thesis contributes to this new and growing area of panel data treatments called "large panel data analysis''. My dissertation consists of three essays: In the first essay, a large panel data model with an omitted factor structure is considered. The role of the factors is to control for the issue of the unobserved time-varying heterogeneity effects. A parameter cascading strategy is proposed to enable efficient estimation of all model parameters when the number of factors is unknown a priori. In the second essay, further models that combine large panel data models with different versions of unobserved latent factors are discussed. Computation-related issues are solved and new specification tests are introduced to check whether or not these factors can be interpreted as classical additive fixed effects. In the third essay, a novel method for estimating panel models with multiple structural changes is proposed. The breaks are allowed to occur at unknown points in time and may affect the multivariate slope parameters individually. Asymptotic results are derived, Monte Carlo experiments are performed, and applications for highlighting these new methods are discussed....

Functional Principal Component Analysis (FPCA) approximates a sample curve as a linear combination of orthogonal basis functions. It is often possible to describe the essential parts of the variations of functional data ...

This thesis consists of three self-contained chapters in applied and theoretical econometrics. The first chapter presents a Lagrange Multiplier test for detecting shock triggered asymmetries in time series and investigates ...

This thesis consists of three self-contained essays in econometrics and statistics. It discusses methodological topics in semi- and nonparametric statistics as well as empirical questions in financial econometrics. My ...

The focus of this thesis is on the estimation of an unknown number of unknown specific locations (points of impact) at which a functional predictor has a specific effect on a real valued dependent variable.<br /> Two general ...