International reserves, growth and effective demand

During the last decade, developing (and some developed) economies have accumulated large amounts of international reserves, mainly for precautionary reasons. This phenomenon has been coupled with moderate economic growth. The resources being amassed largely overwhelm protective needs, there is an excess of resources that is being wasted, and which could be utilised for alternative productive projects, namely to promote growth. If insufficient aggregate demand can largely explain low growth, it is clear that this excess of international reserves can be used to stimulate aggregate demand. This paper argues that the excess of international reserves represents a potential source to boost growth.international reserves; aggregate demand; economic growth

Fernsehen als Ideologie (Teil II). Zur Inszenierung eines Erziehungskonflikts in der "Lindenstrasse"

Die TV-Serie „Lindenstraße“, von der die ARD seit 1985 mehr als 450 Folgen ausstrahlte, gilt als avanciertes Beispiel moderner Fernsehunterhaltung, da sie nicht nur von der Eigenstruktur her „flexibel“ ist, mithin unter Bedingungen und Vorgaben produziert wird, die unmittelbar die Wirkung auf Zuschauer im Blick haben, sondern auch, weil sie als populäre Familienserie einen aufklärenden Anspruch verteidigt und kontinuierlich ein Millionenpublikum bindet. Diese Eigenschaften prädestinieren die „Lindenstraße“ als Untersuchungsobjekt. Mit Hilfe einer qualitativen Inhaltsanalyse werden einige Dialog-Szenen auf Struktur, Geschehen sowie Art der Präsentation hin untersucht. Dadurch lassen sich die feinen Methoden, mit denen die „Lindenstraße“-Macher den Stoff im Dialog, in der Kameraführung, in der Schnitttechnik, in der Regieanweisung sowie in der Auswahl der Themen darbieten, leichter entschlüsseln und erhellen. (DIPF/Orig.

Towards magnetic resonance in scanning tunneling microscopy using heterodyne detection

The present work introduces a new concept for magnetic resonance measurements in the GHz regime inside a scanning tunneling microscope. It is based on heterodyne detection in a spin-polarized tunneling barrier. The experimental requirements, including a new method to suppress transmission effects, are explained. Measurements on three model systems which were studied to validate the new technique are presented and compared to simulations

Towards magnetic resonance in scanning tunneling microscopy using heterodyne detection

The present work introduces a new concept for magnetic resonance measurements in the GHz regime inside a scanning tunneling microscope. It is based on heterodyne detection in a spin-polarized tunneling barrier. The experimental requirements, including a new method to suppress transmission effects, are explained. Measurements on three model systems which were studied to validate the new technique are presented and compared to simulations

Fast and scalable non-parametric Bayesian inference for Poisson point processes

We study the problem of non-parametric Bayesian estimation of the intensity function of a Poisson point process. The observations are $n$ independent realisations of a Poisson point process on the interval $[0,T]$. We propose two related approaches. In both approaches we model the intensity function as piecewise constant on $N$ bins forming a partition of the interval $[0,T]$. In the first approach the coefficients of the intensity function are assigned independent gamma priors, leading to a closed form posterior distribution. On the theoretical side, we prove that as $n\rightarrow\infty,$ the posterior asymptotically concentrates around the "true", data-generating intensity function at an optimal rate for $h$-H\"older regular intensity functions ($0 < h\leq 1$). In the second approach we employ a gamma Markov chain prior on the coefficients of the intensity function. The posterior distribution is no longer available in closed form, but inference can be performed using a straightforward version of the Gibbs sampler. Both approaches scale well with sample size, but the second is much less sensitive to the choice of $N$. Practical performance of our methods is first demonstrated via synthetic data examples. We compare our second method with other existing approaches on the UK coal mining disasters data. Furthermore, we apply it to the US mass shootings data and Donald Trump's Twitter data.Comment: 45 pages, 22 figure

Bayesian wavelet de-noising with the caravan prior

According to both domain expert knowledge and empirical evidence, wavelet coefficients of real signals tend to exhibit clustering patterns, in that they contain connected regions of coefficients of similar magnitude (large or small). A wavelet de-noising approach that takes into account such a feature of the signal may in practice outperform other, more vanilla methods, both in terms of the estimation error and visual appearance of the estimates. Motivated by this observation, we present a Bayesian approach to wavelet de-noising, where dependencies between neighbouring wavelet coefficients are a priori modelled via a Markov chain-based prior, that we term the caravan prior. Posterior computations in our method are performed via the Gibbs sampler. Using representative synthetic and real data examples, we conduct a detailed comparison of our approach with a benchmark empirical Bayes de-noising method (due to Johnstone and Silverman). We show that the caravan prior fares well and is therefore a useful addition to the wavelet de-noising toolbox.Comment: 32 pages, 15 figures, 4 table

Nonparametric Bayesian estimation of a H\"older continuous diffusion coefficient

We consider a nonparametric Bayesian approach to estimate the diffusion coefficient of a stochastic differential equation given discrete time observations over a fixed time interval. As a prior on the diffusion coefficient, we employ a histogram-type prior with piecewise constant realisations on bins forming a partition of the time interval. Specifically, these constants are realizations of independent inverse Gamma distributed randoma variables. We justify our approach by deriving the rate at which the corresponding posterior distribution asymptotically concentrates around the data-generating diffusion coefficient. This posterior contraction rate turns out to be optimal for estimation of a H\"older-continuous diffusion coefficient with smoothness parameter $0<\lambda\leq 1.$ Our approach is straightforward to implement, as the posterior distributions turn out to be inverse Gamma again, and leads to good practical results in a wide range of simulation examples. Finally, we apply our method on exchange rate data sets