arXiv subjects

Christoph Hanck

Publications and source records attributed to Christoph Hanck.

6 recordsLinked to original sources

arXiv2026

A Structural Matrix Autoregression Framework for International Spillovers

Understanding how macroeconomic shocks propagate across countries requires structural models that can jointly identify country-specific shocks and their international transmission. Yet extending structural vector autoregressions (SVARs) to large multi-country systems is challenging due to rapidly increasing dimensionality, computational costs, and the proliferation of identifying restrictions. This paper develops a Bayesian Structural Matrix Autoregression (BSMAR) framework that exploits the natural matrix structure of international macroeconomic data. By separating dependence across economic variables from dependence across countries, the framework provides a parsimonious representation that substantially reduces the dimensionality of large structural systems. We develop a Bayesian sampling algorithm for posterior inference that accommodates zero, sign, and ranking (magnitude) restrictions, allowing established SVAR identification schemes to be combined with a novel approach to identifying contemporaneous international spillovers. Applying the model to quarterly data for 15 economies, we find substantial heterogeneity in international shock transmission, with demand shocks playing a more prominent role than supply shocks in generating cross-country spillovers.

econ.EM↗

arXiv2023

A Data Mining Approach for Detecting Collusion in Unproctored Online Exams

Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we compare our findings to a proctored control group. By this, we establish a rule of thumb for evaluating which cases are "outstandingly similar", i.e., suspicious cases.

cs.CY↗

arXiv2021

Testing for Nonlinear Cointegration under Heteroskedasticity

This article discusses Shin (1994, Econometric Theory)-type tests for nonlinear cointegration in the presence of variance breaks. We build on cointegration test approaches under heteroskedasticity (Cavaliere and Taylor, 2006, Journal of Time Series Analysis) and nonlinearity, serial correlation, and endogeneity (Choi and Saikkonen, 2010, Econometric Theory) to propose a bootstrap test and prove its consistency. A Monte Carlo study shows the approach to have satisfactory finite-sample properties in a variety of scenarios. We provide an empirical application to the environmental Kuznets curves (EKC), finding that the cointegration test provides little evidence for the EKC hypothesis. Additionally, we examine a nonlinear relation between the US money demand and the interest rate, finding that our test does not reject the null of a smooth transition cointegrating relation

econ.EM↗

arXiv2021

When is the best time to learn? -- Evidence from an introductory statistics course

We analyze learning data of an e-assessment platform for an introductory mathematical statistics course, more specifically the time of the day when students learn. We propose statistical models to predict students' success and to describe their behavior with a special focus on the following aspects. First, we find that learning during daytime and not at nighttime is a relevant variable for predicting success in final exams. Second, we observe that good and very good students tend to learn in the afternoon, while some students who failed our course were more likely to study at night but not successfully so. Third, we discuss the average time spent on exercises. Regarding this, students who participated in an exam spent more time doing exercises than students who dropped the course before.

cs.CY↗

arXiv2021

Effects of Early Warning Emails on Student Performance

We use learning data of an e-assessment platform for an introductory mathematical statistics course to predict the probability of passing the final exam for each student. Subsequently, we send warning emails to students with a low predicted probability to pass the exam. We detect a positive but imprecisely estimated effect of this treatment, suggesting the effectiveness of such interventions only when administered more intensively.

cs.CY↗

arXiv2018

Towards digitalisation of summative and formative assessments in academic teaching of statistics

Web-based systems for assessment or homework are commonly used in many different domains. Several studies show that these systems can have positive effects on learning outcomes. Many research efforts also have made these systems quite flexible with respect to different item formats and exercise styles. However, there is still a lack of support for complex exercises in several domains at university level. Although there are systems that allow for quite sophisticated operations for generating exercise contents, there is less support for using similar operations for evaluating students' input and for feedback generation. This paper elaborates on filling this gap in the specific case of statistics. We present both the conceptional requirements for this specific domain as well as a fully implemented solution. Furthermore, we report on using this solution for formative and summative assessments in lectures with large numbers of participants at a big university.

cs.HC↗