873 publications from this institution
This article 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 D urbin model ( SDM ) specification in this study that nests most models used in the regional growth literature, and develop a simple B ayesian model‐averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on previous work by reducing the computational costs through the use of B ayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial D urbin 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 that vanishes for the case of a spatial weight matrix with a trace of zero. This allows for a larger domain of spatial growth regression models to be analyzed 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 data set of income per capita growth for 273 E uropean regions.
Despite the research conducted in recent years in the field of information and communication economics, there is relatively little understanding of the imp
Iron platelets containing known contents of manganese were reacted with gas phases of controlled pH2S/pH2 ratios. After quenching, the sulfur contents of t
ISHS VII International Workshop on Fire Blight RESULTS OF RESISTANCE TESTS TO ERWINIA AMYLOVORA (BURRILL) WINSLOW ET AL. OF MALUS AND PYRUS PROGENIES WITHIN THE ROOTSTOCK SELECTION PROGRAMME
The focus of this study is on regional knowledge production activities in Europe, with special emphasis on the interplay between agglomeration and network effects. As increasingly considered in economic geography and regional science in the recent past, regional knowledge production activities, on the one hand, still remain geographically bounded; on the other hand, knowledge production activities have become increasingly interwoven and internationalized, emphasizing the crucial importance of region-external knowledge sources for a region’s knowledge production capacity. The objective of the study is to estimate to what extent agglomeration and network effects influence knowledge production activities at the level of European regions. We use an extended regional knowledge production function framework as basis for the study, and derive a spatial Durbin model (SDM) relationship that can be used for empirical testing. The European coverage is achieved using 241 NUTS-2 regions covering the EU-25 member states. The dependent variable, knowledge production activity, is measured in terms of patent counts at the regional level in the time period 1998-2008, using patents applied at the European Patent Office (EPO). The independent variables include an agglomeration index, measured in terms of population density, and the regional participation intensity in the European network of R&D cooperation, measured in terms of the number of participations of a region in R&D joint ventures funded by the European Commission under the heading of the EU Framework programs (FPs). By this we are able to estimate the distinct effects of network participation and agglomeration on regional knowledge production. In our modeling framework, we further control for total regional R&D expenditures as widely used in regional knowledge production function frameworks and its empirical applications. In estimating the effects, we implement a panel version of the standard SDM that controls for spatial autocorrelation as well as individual heterogeneity across regions. The specification incorporates a spatial lag of the dependent variable as well as spatial lags of the independent variables. This allows for the estimation of spatial spillovers of agglomeration and network effects from neighboring regions by calculating scalar summary measures of impacts. The estimation results are expected to provide sketches of policy implications in a European and regional policy context. JEL Classification: R11, O31, C21 Keywords: Regional knowledge production, Agglomerations effects, R&D networks, European Framework Programs, knowledge production function, panel spatial Durbin model
No abstract is provided for this article.
In this paper, we explore the relationship between state-level household income inequality and macroeconomic uncertainty in the United States. Using a novel large-scale macroeconometric model, we shed light on regional disparities of inequality responses to a national uncertainty shock. The results suggest that income inequality decreases in most states, with a pronounced degree of heterogeneity in terms of shapes and magnitudes of the dynamic responses. By contrast, some few states, mostly located in the West and South census region, display increasing levels of income inequality over time. We find that this directional pattern in responses is mainly driven by the income composition and labor market fundamentals. In addition, forecast error variance decompositions allow for a quantitative assessment of the importance of uncertainty shocks in explaining income inequality. The findings highlight that volatility shocks account for a considerable fraction of forecast error variance for most states considered. Finally, a regression-based analysis sheds light on the driving forces behind differences in state-specific inequality responses.
The design of a spatial framework in multi-and interregional modelling is a crucial element of the research process. In this paper an attempt is made to pr
An algorithm of incremental approximation of functions in a normed linearspace by feedforward neural networks is presented. The concept of variationof a function with respect to a set is used to estimate the approximationerror together with the weight decay method, for optimizing the size andweights of a network in each iteration step of the algorithm. Two alternatives, recursively incremental and generally incremental, are proposed. In the generally incremental case, the algorithm optimizes parameters of all units in the hidden layer at each step. In the recursively incremental case, the algorithm optimizes the parameterscorresponding to only one unit in the hidden layer at each step. In thiscase, an optimization problem with a smaller number of parameters is beingsolved at each step.
Keywords: modelling spaciale ; simulation ; integration Reference Record created on 2005-06-20, modified on 2016-08-08
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.
For the mitigation of severe accidents, the European Pressurized Water Reactor (EPR) has adopted and improved the defense-in-depth approaches of its predecessors, the French “N4” and the German “Konvoi” plants. Beyond the corresponding evolutionary changes, the EPR includes a new, 4th level of defense-in-depth that is aimed at limiting the consequences of a postulated severe accident with core melting. It involves a strengthening of the confinement function and the avoidance of large early releases. The latter requires the prevention of scenarios and events that can result in high loads on the containment, e.g., a failure of the Reactor Pressure Vessel (RPV) at high internal pressure. This is achieved by dedicated design measures. The paper gives an short overview of the general concept and the strategies for: primary circuit depressurization, H2 mitigation and the avoidance of energetic Fuel Coolant Interactions (FCIs). It then describes, in detail, the conceptual solution for the stabilization and long-term cooling of the molten core. The EPR melt retention strategy supports itself on the use of an ex-vessel core catcher located in a compartment lateral to the pit. The related spatial and functional separation isolates the core catcher from the various loads during RPV failure and, at the same time, avoids risks resulting from an unintended initiation of the system during power operation. Within the core catcher, the melt will be passively flooded with water from the Internal Refueling Water Storage Tank (IRWST). Due to the effective cooling of the melt from all sides a stable state will be reached within hours and complete solidification of the melt is achieved after a few days. The core catcher can optionally be supplied by the Containment Heat Removal System (CHRS). In this active mode of operation, the water levels inside spreading compartment and reactor pit rise and the pools become subcooled, so further steaming is avoided. This results in a depressurization of the containment in the long-term.
In addition to the alendronate Osteoporosis Intervention Trial (FOSIT) core protocol 901–0A of 1908 enrolled patients, the use of peripheral quantitative computed tomography (pQCT) was explored for the assessment of response to therapy. Bone mineral and strength related parameters at two different sites at the distal radius were explored in a subset of the multicenter core study. One hundred and three patients were entered into the substudy and given either a daily dose of 10 mg of alendronate or placebo for 1 year. Measurements were done at months 0, 3, 6, and 12. Inclusion criteria were bone mineral density (BMD) measurements at the lumbar spine of −2 SD. The response to therapy was assessed by dual-energy X-ray absorptiometry in the lumbar spine and the hip, and by pQCT in the ultradistal and the shaft sites of the radius. In line with the FOSIT core study, alendronate increased BMD at the lumbar spine and the hip, and it decreased the serum biochemical markers of bone turnover. The substudy showed differences between the therapy and placebo group in trabecular bone density (8.4%, p = 0.095), in total density (6.8%, p = 0.009), and in the bone strength index (BSI) (15.6 mm3, p = 0.037) at the ultradistal site due to treatment and no changes at the radius shaft. A significant correlation was observed between percentage changes from baseline in BMD of the lumbar spine, and in total density and bone strength at the ultradistal radius site in the treatment group, but not in the placebo group. Thus, the ultradistal radius site did respond to alendronate therapy. The increased bone density accompanied a significant gain in the BSI at the ultradistal site, a finding that might help explain the reduced wrist fractures in the alendronate Fracture Intervention Trial.
Spatial interaction modelling is a well established field in geography and regional science. Since the pioneering work of Wilson (1970) on entropy maximization, however, there has been surprisingly little innovation in the design of spatial interaction models. The...
Five biosynthetically related bufadienolides were isolated from Kalanchoe daigremontiana Hamet et Perr. and structurally elucidated by X-Ray and NMR techniques. Two compounds were orthoacetates which showed strong CNS activity.
The pressures of global competition are affecting regions throughout the world and making it increasingly necessary to understand the complex underlying mechanisms and the potential for innovation offered by new technology. Success in economic restructuring depends not only on the technology itself, but the professional and entrepreneurial skills available and the support of provided by institutions and information networks. The very local nature these phenomena, which are critical to the innovative propensity of firms operating within the region, introduces an inevitable spatial dimension. The time therefore seems ripe to bring together contributions from scholars working in different, but related disciplines, with the aim of investigating the triangular relationship between technological change, economic development and space. The present volume offers a compact review of current theoretical developments and valuable insights deriving from recent empirical studies carried out both within Europe and elsewhere. All those contributing to this volume are actively involved in research in the field. Without their intellectual contribution and willingness to participate in this joint project, the book would not have been possible. We should like, in addition, to thank Angela Spence for her capable assistance in coordinating the various stages of preparation of the book, as well as her translation work and careful linguistic editing. Thanks also go to Paola Stasi for her meticulous copy editing and help in preparing the indices. Their work has been invaluable in moulding together in a single volume contributions from so many different sources.
