The demand for euro area currencies: past, present and future

The present paper analyses currency in circulation in the euro area since the beginning of the 1980s. After a comprehensive literature review on this topic we present some stylised facts on currency holdings in the euro area countries as well as at an aggregate euro area level. The next chapter develops a theoretical model, which extends traditional money demand models to also incorporate arguments for the informal economy and foreign demand for specific currencies. In the empirical sections we first estimate the demand for euro legacy currencies in total and for small and large denominations within a cointegration framework. We find significant differences between the determinants of holdings of small and large denominations as well as overall currency demand. While small-value banknotes are mainly driven by domestic transactions, the demand for large-value banknotes depends on a short-term interest rate, the exchange rate of the euro as a proxy for foreign demand and inflation variability. Large-value banknotes seem to be therefore used to an important extent as a store of value domestically and abroad. As monetary policy is mainly interested in getting information on the demand for currency used for domestic transactions we also try several approaches in this direction. All the methods applied result in rather low levels of transaction balances used within the euro area of around 25% to 35% of total currency. After this we deal with possibly changing cost-benefit-considerations of the use of cash due to the introduction of euro notes and coins. Overall, there seems no evidence so far of a substantial decline of the demand for currency in the euro area. JEL Classification: E41, E52, E58cointegration, currency in circulation, purposes of holding currency

Evaluation of neural network pattern classifiers for a remote sensing application

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing setshttps://ssrn.com/abstract=1523788%20or%20http://dx.doi.org/10.2139/ssrn.1523788Published versio

The Dynamic Impact of Monetary Policy on Regional Housing Prices in the United States

This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary policy shocks on housing prices. To simultaneously estimate the model parameters and unobserved factors we rely on Bayesian estimation and inference. Policy shocks are identified using high-frequency surprises around policy announcements as an external instrument. Impulse response functions reveal differences in regional housing price responses, which in some cases are substantial. The heterogeneity in policy responses is found to be significantly related to local regulatory environments and housing supply elasticities. Moreover, housing prices responses tend to be similar within states and adjacent regions in neighboring states

Neufunde von Arten und Unterarten des Festuca-ovina-Aggregates in Trockenrasen an der Mittleren Elbe und im angrenzenden Gebiet

Im Gebiet der Mittleren Elbe gehören Dünen, Talsande, holozäne sandige Flußablagerungen im Elbvorland, Moränen und Deiche mit Trockenrasen zu den charakteristischen Naturraumelementen. Arten des Festuca-ovina-Aggregates spielen in diesen Trockenrasen eine große Rolle. Im Rahmen von vegetationsökologischen Bearbeitungen der Trockenrasen im Biosphärenreservat „Flußlandschaft Elbe“ und im Stendaler Raum konnten zahlreiche Neufunde von Festuca-ovina-Sippen gemacht werden (s. a. FISCHER 1996, 1998, PROJAHN 1998). Außerdem wurde ein vorliegender Fund von vor 1950 (BENKERT et al. 1996) aktuell wieder bestätigt. Die Verbreitungskarten bei BENKERT et al. (1996), HAEUPLER & SCHÖNFELDER (1988) und einige Angaben in STOHR (1990) sind um diese Neufunde für Festuca brevipila, F. filiformis, F. ovina ssp. guestfalica, F. ovina ssp. ovina, F. polesica, F. rupicola und insbesondere für F. pulchra (= F. pseudovina) zu ergänzen. Im folgenden werden nur Fundorte aufgeführt, wenn in den zugehörigen Meßtischblättern bei HAEUPLER & SCHÖNFELDER (1988) sowie in den Meßtischblatt-Quadranten bei BENKERT et al. (1996) Punkte fehlen oder die Sippen nicht behandelt werden. Dies gilt beispielsweise für die verschiedenen Festuca-brevipila-Formen, Festuca ovina ssp. guestfalica und F. ovina ssp. ovina sowie die abweichenden Formen von F. polesica und F. psammophila. Die Fundorte liegen zum größten Teil in Sachsen-Anhalt. Bei Fundorten in anderen Bundesländern werden diese mit den folgenden Abkürzungen bezeichnet: ME = Mecklenburg-Vorpommern, NS = Niedersachsen und BR = Brandenburg. An allen genannten Fundorten wurden Belege entnommen. Wenn nicht anders angegeben, wurde das Material von P. FISCHER gesammelt. Die Belege wurden von G. STOHR bis auf einige Exemplare von Festuca brevipila und Festuca ovina ssp. guestfalica bestätigt bzw. bestimmt. An dieser Stelle möchten wir für die Überlassung von Fundortsangaben Frau D. PROJAHN (Schernikau) und Herrn S. NICKOLMANN (Magdeburg) danken

Development of a pressure stable inline droplet generator with live droplet size measurement

For the research on droplet deformation and breakup in scaled high-pressure homogenizing units, a pressure stable inline droplet generator was developed. It consists of an optically accessible flow channel with a combination of stainless steel and glass capillaries and a 3D printed orifice. The droplet size is determined online by live image analysis. The influence of the orifice diameter, the mass flow of the continuous phase and the mass flow of the disperse phase on the droplet diameter were investigated. Furthermore, the droplet detachment mechanisms were identified. Droplet diameters with a small diameter fluctuation between 175 µm and 500 µm could be realized, which allows a precise adjustment of the capillary (Ca) and Weber (We) Number in the subsequent scaled high pressure homogenizer disruption unit. The determined influence of geometry and process parameters on the resulting droplet size and droplet detachment mechanism agreed well with the literature on microfluidics. Furthermore, droplet trajectories in an exemplary scaled high-pressure homogenizer disruption unit are presented which show that the droplets can be reinjected on a trajectory close to the center axis or close to the wall, which should result in different stresses on the droplets

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay

The piranha genome provides molecular insight associated to its unique feeding behavior

The piranha enjoys notoriety due to its infamous predatory behavior but much is still not understood about its evolutionary origins and the underlying molecular mechanisms for its unusual feeding biology. We sequenced and assembled the red-bellied piranha (Pygocentrus nattereri) genome to aid future phenotypic and genetic investigations. The assembled draft genome is similar to other related fishes in repeat composition and gene count. Our evaluation of genes under positive selection suggests candidates for adaptations of piranhas’ feeding behavior in neural functions, behavior, and regulation of energy metabolism. In the fasted brain, we find genes differentially expressed that are involved in lipid metabolism and appetite regulation as well as genes that may control the aggression/boldness behavior of hungry piranhas. Our first analysis of the piranha genome offers new insight and resources for the study of piranha biology and for feeding motivation and starvation in other organisms

The dynamic impact of monetary policy on regional housing prices in the United States

This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary policy shocks on housing prices across metropolitan and micropolitan regions. To simultaneously estimate the model parameters and unobserved factors we rely on Bayesian estimation and inference. Policy shocks are identified using high-frequency suprises around policy announcements as an external instrument. Impulse reponse functions reveal differences in regional housing price responses, which in some cases are substantial. The heterogeneity in policy responses is found to be significantly related to local regulatory environments and housing supply elasticities. Moreover, housing prices responses tend to be similar within states and adjacent regions in neighboring states.Series: Working Papers in Regional Scienc

Evaluation of Neural Pattern Classifiers for a Remote Sensing Application

This paper evaluates the classification accuracy of three neural network classifiers on a satellite image-based pattern classification problem. The neural network classifiers used include two types of the Multi-Layer-Perceptron (MLP) and the Radial Basis Function Network. A normal (conventional) classifier is used as a benchmark to evaluate the performance of neural network classifiers. The satellite image consists of 2,460 pixels selected from a section (270 x 360) of a Landsat-5 TM scene from the city of Vienna and its northern surroundings. In addition to evaluation of classification accuracy, the neural classifiers are analysed for generalization capability and stability of results. Best overall results (in terms of accuracy and convergence time) are provided by the MLP-1 classifier with weight elimination. It has a small number of parameters and requires no problem-specific system of initial weight values. Its in-sample classification error is 7.87% and its out-of-sample classification error is 10.24% for the problem at hand. Four classes of simulations serve to illustrate the properties of the classifier in general and the stability of the result with respect to control parameters, and on the training time, the gradient descent control term, initial parameter conditions, and different training and testing sets. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc

The dynamic impact of monetary policy on regional housing prices in the US: Evidence based on factor-augmented vector autoregressions

In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.Series: Working Papers in Regional Scienc