The financial situation of municipalities is often dire. North Rhine-Westphalia, Germany’s biggest state, is generally thought to be in the worst financial situation. High per capita debt is often accompanied by a high share of short-term debt which strongly exposes the municipalitiy to interest rate risk.
Dynamic charts are a nice way to visualize patterns over time. With the googleVis package this kind of graph has become easy to create. In this post, I will use the googleVis package to create two interactive moving bubble charts from the Global Terrorism Database (GTD).
In this post I will explore, how correlations between long- and short-term stock movements can be visualized using R and ggplot2. I will look at the current German composite index DAX, but any other set of stocks–for which data are available–is feasible.
In this blog entry, I will use data from the Global Terrorism Database to briefly explore some aspects of how the composition of terrorist events has changed over time.
When it comes to causality tests, the typical Granger-causality test can be problematic. Testing for Granger-causality using F-statistics when one or both time series are non-stationary can lead to spurious causality (He & Maekawa, 1999).
Empirical contributions that focus on terrorism and its reception by the media face the fundamental difficultly of quantifying media coverage of terrorism, which has led to a relatively thin body of empirical research on the subject: