Economics of Ethical and Prosocial Behavior: Experiments on Social Responsibility, Ignorance, and Cheating

In this thesis I experimentally investigate prosocial and ethical behavior in economic interactions. The thesis consists of three experimental research papers that have a broad range of research questions on social responsibility, ignorance and cheating. With these experiments I aim to better understand when and why people behave ethically and/or prosocially and which consequences it has on their own and other players’ payoffs, and on overall efficiency. The results from the three experimental studies suggest that (i) donations to charity by employees are rewarded in an experimental setting, and the effect is driven by reciprocal concerns; (ii) there is a significant fraction of people who decide not to know about negative consequences of own actions, and the sorting of social agents of a low type into ignorance drives self-interested behavior of ignorant agents; and (iii) if the possibility of being exposed as a liar is small, the tendency to lie increases with incentives, indicating that some people have positive and finite costs of lying. Furthermore, when the participants lie, they lie to the full extent, which suggests that the intrinsic cost of lying is fixed.

Hilbert Space Valued Signal Plus Noise Models: Analysis of Structural Breaks under High Dimensionality and Temporal Dependence

This thesis is concerned with change point analysis for time series, i.e. with detection of structural breaks in time-ordered, random data. This long-standing research field regained popularity over the last few years and is still undergoing, as statistical analysis in general, a transformation to high-dimensional problems. We focus on the fundamental »change in the mean« problem and provide extensions of the classical non-parametric Darling-Erdős-type cumulative sum (CUSUM) testing and estimation theory within highdimensional Hilbert space settings. In the first part we contribute to (long run) principal component based testing methods for Hilbert space valued time series under a rather broad (abrupt, epidemic, gradual, multiple) change setting and under dependence. For the dependence structure we consider either traditional m-dependence assumptions or more recently developed m-approximability conditions which cover, e.g., MA, AR and ARCH models. We derive Gumbel and Brownian bridge type approximations of the distribution of the test statistic under the null hypothesis of no change and consistency conditions under the alternative. A new formulation of the test statistic using projections on subspaces allows us to simplify the standard proof techniques and to weaken common assumptions on the covariance structure. Furthermore, we propose to adjust the principal components by an implicit estimation of a (possible) change direction. This approach adds flexibility to projection based methods, weakens typical technical conditions and provides better consistency properties under the alternative. In the second part we contribute to estimation methods for common changes in the means of panels of Hilbert space valued time series. We analyze weighted CUSUM estimates within a recently proposed »high-dimensional low sample size (HDLSS)« framework, where the sample size is fixed but the number of panels increases. We derive sharp conditions on »pointwise asymptotic accuracy« or »uniform asymptotic accuracy« of those estimates in terms of the weighting function. Particularly, we prove that a covariance-based correction of Darling-Erdős-type CUSUM estimates is required to guarantee uniform asymptotic accuracy under moderate dependence conditions within panels and that these conditions are fulfilled, e.g., by any MA(1) time series. As a counterexample we show that for AR(1) time series, close to the non-stationary case, the dependence is too strong and uniform asymptotic accuracy cannot be ensured. Finally, we conduct simulations to demonstrate that our results are practically applicable and that our methodological suggestions are advantageous.