873 publications from this institution
In den vergangenen Jahren sind verschiedene Geräte und Untersuchungstechniken zur Messung des Knochenmineralgehaltes entwickelt worden. Seit kurzem steht auch in Deutschland eine neue Gerätegeneration zur Verfügung [2], die eine Bestimmung des...
Die ursprüngliche Naumburger Malus-Artenkollektion wurde in das Institut für Obstforschung Dresden-Pillnitz überführt. Sie umfaßt
This paper uses a global vector autoregressive (GVAR) model to analyze the relationship between FDI inflows and output dynamics in a multi-country context. The GVAR model enables us to make two important contributions: First, to model international linkages among a large number of countries, which is a key asset given the diversity of countries involved, and second, to model foreign direct investment and output dynamics jointly. The country-specific small-dimensional vector autoregressive submodels are estimated utilizing a Bayesian version of the model coupled with stochastic search variable selection priors to account for model uncertainty. Using a sample of 15 emerging and advanced economies over the period 1998:Q1 to 2012:Q4, we find that US outbound FDI exerts a positive long-term effect on output. Asian and Latin American economies tend to react faster and also stronger than Western European countries. Forecast error variance decompositions indicate that FDI plays a prominent role in explaining GDP fluctuations, especially in emerging market economies. Our findings provide evidence for policy makers to design macroeconomic policies to attract FDI inflows in the respective countries.
No abstract is provided for this article.
The objective of this study is to identify knowledge spillovers that spread across regions in Europe and vary in magnitude for different industries. The study uses a panel of 203 NUTS-2 regions covering the 15 pre-2004 EU-member-states to estimate the impact over the period 1998-2003, and distinguish between five major industries. The study implements a fixed effects panel data regression model with spatial autocorrelation to estimate effects using patent applications as a measure of R&D output to capture the contribution of R&D (direct and spilled-over) to regional productivity at the industry level. The results suggest that interregional knowledge spillovers and their productivity effects are to a substantial degree geographically localised and this finding is consistent with the localisation hypothesis of knowledge spillovers. There is a substantial amount of heterogeneity across industries with evidence that two industries (electronics, and chemical industries) produce interregional knowledge spillovers that have positive and highly significant productivity effects. The study, moreover, confirms the importance of spatial autoregressive disturbance in the fixed effects model for measuring the TFP impact of interregional knowledge spillovers at the industry level. (authors' abstract)
This paper considers the most important aspects of model uncertainty for spatial regression models, namely the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on the work by LeSage and Fischer (2008) by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation and integration of the matrix exponential. In particular, the matrix exponential has a simple matrix determinant which vanishes for the case of a spatial weight matrix with a trace of zero (LeSage and Pace 2007). This allows for a larger domain of spatial growth regression models to be analysed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity-based, k-nearest neighbor and distance-based spatial weight matrices), using a dataset of income per capita growth for 273 European regions.
Safety problems associated with the use of gaseous hydrogen as an energy carrier are compared with other combustibles (methane, propane). On the basis of a simple one-dimensional model, the whole combustion process can be simulated. Critical burning and flame velocities and the critical strength for incident shock waves for transition from deflagration-to-detonation are calculated. Flame inhibition methods are briefly discussed. The envisaged large-scale introduction of hydrogen as an energy carrier into energy supply systems raises questions concerning specific risks and necessary safety measures in a much greater extent and significance as compared to the exclusive industrial utilization of hydrogen.
Most transportation research techniques and methods currently in use were developed in the 1960s and 1970s, i.e. in an era of scarce computing power and small data sets. Their implementations take only limited advantage of the data storage and retrieval capabilities of modem computational techniques, and basically ignore both the emerging new era of parallel supercomputing and the computational intelligence techniques. This chapter provides a small sized real world example for interregional telecommunication traffic modelling and illustrates its superiority compared with the standard statistical benchmark. It considers some fundamental characteristics of these computational neural networks (CNN). The chapter focuses on feedforward neural networks which provide transportation researchers with a novel and extremely useful class of mathematical tools. It deals with supervised training of such networks and reviews some powerful (local) optimization techniques. Multilayer feedforward CNNs such as perceptrons and radial basis function networks have emerged as attractive class of CNNs based upon sound theoretical concepts.
Over the last few years, rising prices and increasing price volatility of major agricultural food commodities were observed. This caused a debate among various organizations about who is responsible for this development. While many Non-Governmental Organizations proclaim that speculations in future markets cause the rise in food prices, academic research provides ambiguous results on this topic. This controversy is the motivation for this study. In order to offer additional insights, the relationship between the price changes of corn, wheat, and soybeans and the corresponding changes in open interests are analyzed. Commitments of Traders as well as Disaggregated Commitments of Traders reports are investigated to determine whether the activities of speculators adversely affect food prices. First, Johansen cointegration tests are employed to analyze the relationship between price and position data. Second, VAR and VECM are used to analyze short- and long-term dynamics. The results of the empirical analysis demonstrate that in the short-run price changes precede changes in open interest. Additionally, soybeans show a long-run equilibrium relationship between both series, indicating that speculators influenced past prices to some extent. However, the percentage price change is rather low. Therefore, sharp rises in soybean prices cannot be explained by it.
Exploratory spatial data analysis is often a preliminary step to more formal modelling approaches that seek to establish relationships between the observations of a variable and the observations of other variables, recorded for each areal unit. The focus in this...
Although there is a substantial body of literature on labour market analysis, most of it ignores the spatial dimension of the labour market. A spatial perspective in analysing labour market processes is important for several reasons. FIRST, labour markets are by no means as homogeneous as conventional labour market theories assume. SECOND, most countries are displaying strong regional variations in the dynamics of unemployment. THIRD, geographical space exerts a frictional effect on labour market processes. Regional unemployment rates appear to be the most important indicators for analysing labour market processes from a spatial perspective. The paper aims to discuss some of the problems that are associated with the use of regional unemployment rates. We will focus attention on conceptual problems, problems of data quality and on some of the new problems that have arisen due to the widespread use of new computer technology. Solutions to many of the problems are obvious, but many of the new problems will require some extra effort for their solution. The tyranny that threatens the research community is that regional unemployment data exercise a power over us that can lead the naive to misinterpretations. The data may mislead even the most righteous among us. A good deal of research effort is often given to overcome the tyranny that is found in the columns and rows that the lay public likes to call statistics. The discussion will be enriched by means of a study utilizing regional unemployment rates at the district level in West Germany.
No abstract is provided for this article.
Pulsatilla styriaca (P. halleri subsp. styriaca) , up to now considered endemic to Styria within Austria, is reported as new for the Bulgarian flora. Earlier, it had been identified as P. halleri s. str. (P. halleri subsp. halleri) because it is clearly different from P. rhodopaea (P. halleri subsp. rhodopaea) distributed in Bulgaria. The only three populations situated in western Sredna Gora (W Balkan mts.) are small and have been monitored during the period 1998–2013. Population sizes are decreasing and the species thus endangered in Bulgaria. By morphological (phytographical) evidence, using features of traditional Pulsatilla taxonomy, the differences between the Bulgarian and Styrian populations of P. styriaca , in respect to the variation amplitude, proved to be negligible. This taxon, however, turned out to be conspecific with P. subslavica distributed endemically in Slovakia. This is demonstrated by a comparative survey of Slovak specimens attributed to this species and specimens of P. styriaca from Styria. Consequently, P. styriaca is no longer endemic to Styria and Austria but exhibits a highly disjunct distribution range covering western central Slovakia, eastern Austria (Styria) and western Bulgaria. Bulgarian, Slovakian and Austrian habitats of this species are compared und the conservation status is discussed.
No abstract is provided for this article.
Although it is agreed that the dual development of monetary integration and territorial enlargement are likely to generate profound effects on European spatial structure, in both West and East, much uncertainty centres around the question of what changes will be brought about. This book furthers our economic understanding of the opportunities and challenges offered by these developments. The emphasis is primarily on the economic agenda associated with European integration. Part A reviews the debate on European monetary unification. Economic integration raises many issues, one which is dealt in depth is the issue of convergence versus divergence. Part B centres around the dynamics of cohesion in the EU and the associated regional policies, reflecting on experience from the past and challenges for the future. Part C sheds some light on the complexities of transition and integration of Central and Eastern European countries, the second major challenge being faced by the EU at the turn of the century.
In this paper we present a formal analysis that incorporates returns to transportation into a Ricardian framework to predict trade patterns. The important point gained from this analysis is that increasing returns to transportation, coupled with appropriate distances between trading partners, can be shown to reverse Ricardian predictions even when there are no international differences in tastes, technology, or factor endowments. Additional gains from trade may emerge from reductions in aggregate delivery costs owing to scale economies.
"Chapter 8 Neural networks: a class of flexible non-linear models for regression and classification" published on 27 Mar 2015 by Edward Elgar Publishing.
The focus here is on the log-normal version of the spatial interaction model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination-flows with formal approaches that allow for two different types of spatial dependence in flow magnitudes. Endogenous interaction reflects situations where there is reaction to feedback regarding flow magnitudes from regions neighboring origin and destination regions. This type of interaction can be modeled using specifications proposed by LeSage and Pace (2008) who use spatial lags of the dependent variable to quantify the magnitude and extent of feedback effects, hence the term endogenous interaction.
