Publications

A Choice-Theoretical Labour-Market Model: Empirical Tests at the Mesolevel

This paper contains an analysis of a multiregional labour-supply model for Austria. In the approach suggested here attempts are made to combine the advantages of random-utility-based discrete choice theory and partial reduced-form estimation. Two recursive submodels, the labour-force participation submodel, and the commuting and employment submodel, are developed. Three different types of model specifications at the mesolevel are used to analyse the consequences of choosing a spatial framework of overlapping regional labour markets upon the model parameters, to investigate the usefulness of the model approach in relation to different regionalisation variants, and to carry out a sensitivity analysis with respect to the effects of the model parameters used to delineate the labour-market regions.

Trade Effects of the Emerging Market Economies: A Study of the Transport Potential of the Rhine-Main-Danube Waterway

During the last few years, the economic landscape of Europe has changed dramatically, both in the East and in the West. In Eastern Europe, the peaceful revolutions — except Yugoslavia — are among the most dramatic events of the current epoch. A central...

Knowledge spillovers and total factor productivity. Evidence using a spatial panel data model

This article investigates the impact of knowledge capital stocks on total factor productivity (TFP) through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross‐region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross‐region knowledge spillovers on TFP in Europe. The dependent variable is the region‐level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen, and Diewert (1982). This index describes how efficiently each region transforms physical capital and labor into output. The explanatory variables are internal and out‐of‐region stocks of knowledge, the latter capturing the contribution of cross‐region knowledge spillovers. We construct patent stocks to proxy annual regional knowledge capital stocks for N=203 regions during 1997–2002. In estimating the effects, we implement a spatial panel data model that controls for spatial autocorrelation as well as individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions and add an important spatial dimension to discussions in the literature by showing that productivity effects of knowledge spillovers increase with geographic proximity.

HIV-Präexpositionsprophylaxe (PrEP) im ländlichen Raum – ein niedrigschwelliges Präventionsangebot des Gesundheitsamts Rottweil (Baden-Württemberg)

Measurement of the effects of regional policy by means of time series analysis

Estimates and Inferences of Knowledge Capital Impacts on Regional Total Factor Productivity

This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. We provide an econometric derivation of the relationship and show that the presence of latent/unobservable regional knowledge capital leads to a model relationship that includes both spatial and technological dependence. This model specification accounts for both spatial and technological dependence between regions, which allows us to quantify spillover impacts arising from both types of interaction. Sample data on 198 NUTS-2 regions spanning the period from 1997 to 2002 was used to empirically test the model, to measure both direct and indirect effects of knowledge capital on regional total factor productivity, and to assess the relative importance of knowledge spillovers from spatial versus technological proximity.

The spatial autocorrelation problem in spatial interaction modelling: a comparison of two common solutions

Spatial interaction models of the gravity type are widely used to describe origin-destination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterize the origin region of interaction, variables that characterize the destination region of interaction, and variables that measure the separation between origin and destination regions. A violation of standard minimal assumptions for least squares estimation may be associated with two problems: spatial autocorrelation within the residuals, and spatial autocorrelation within explanatory variables. This paper compares a spatial econometric solution with the spatial statistical Moran eigenvector spatial filtering solution to accounting for spatial autocorrelation within model residuals. An example using patent citation data that capture knowledge flows across 257 European regions serves to illustrate the application of the two approaches.

Theory of Comparative Advantage: Do Transportation Costs Matter?

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.

The Speed of Income Convergence in Europe: A case for Bayesian Model Averaging with Eigenvector Filtering

The speed of income convergence in Europe remains one of the hot topics in regional economics. Recently Bayesian Model Averaging (BMA) applied to spatial autoregressive models seems to have gained more popularity. BMA averages over some predetermined number of so called top models, ranked by the model's posterior likelihood. We regard two approaches for especially noteworthy: First Crespo-Cuaresma and Feldkircher (2012) employ BMA to a spatial autoregressive model, where spatial eigenvector filtering is used in order to tackle the econometric problems caused by the spatial lag. However, spatial filtering has its drawbacks. It relies on a model approximation and no partial derivatives of interest associated with the model can be computed. This means, that it is impossible to derive direct and indirect effects. Second LeSage and Fischer (2008) rely on BMA applied to a Spatial Durbin Model (SDM), where the model posterior is calculated without any model approximation. Although it can be computationally burdensome, it allows for a proper model interpretation if the underlying data generating process (DGP) is of SDM form. One virtue of spatial filtering, as shown by Pace et al. (2011), is that it estimates some of the model coefficients efficiently for various spatial autocorrelated DGPs. Hence, the likelihoods associated with spatial filtering are more robust against model misspecification. Since our preliminary results show that the top models' (posterior) likelihoods obtained from a spatial filtering BMA exercise and (non spatial filtering) BMA applied to a Spatial Durbin Model differ, it is most likely that the DGP is not of SDM form, i.e. misspecified. This leads us to the conclusion that, even though the methodology employed by Crespo-Cuaresma and Feldkircher (2012) cannot be used for a proper model interpretation, the results obtained by spatial filtering BMA do not suffer from model misspecification. References: Crespo-Cuaresma, J., Feldkircher, M. (2012), `Spatial Filtering, Model Uncertainty and the Speed of Income Convergence in Europe', Journal of Applied Econometrics, forthcoming. LeSage, J., Fischer, M. (2008), `Spatial Growth Regressions, Model Specification, Estimation, and Interpretation', Spatial Economic Analysis 3, 275-304. Pace, R., LeSage, J., Zhu, S. (2011), `Interpretation and Computation of Estimates from Regression Models using Spatial Filtering', written for Spatial Econometrics Association 2011. Keywords: Model uncertainty, spatial ltering, determinants of economic growth, European regions. JEL Classi cations: C11, C21, O11, R11

The innovation process and network activities of manufacturing firms: Conceptual considerations and empirical evidence from the metropolitan region of Vienna

Innovation networks of manufacturers are currently receiving much attention as a competitive strategy. The contribution represents a response to the growing recognition that there are very important reasons why we need a better understanding of the relationship between innovation and networking. This response is conceptual in form, but enriched with some empirical evidence from the metropolitan region of Vienna. The paper demonstrates unambiguously the importance of external network activities during the innovation process that are organized around five types of networks: customer networks, manufacturing supplier networks, producer service supplier networks, producer networks and co-operation with research institutions and departments of universities. The data clearly indicate that networking is not only and primarily a metropolitan phenomenon. Spatial proximity is just one, but evidently not the decisive criterion for innovation-oriented relationships. The geography of networking largely extends to national and international levels.

Nuklearmedizinische Therapie

Für die Therapie mit offenen Radionukliden oder Radiopharmaka ist grundsätzlich eine spezifische intratumorale Speicherung oder ein hoher Tumor/Background/Quotient erforderlich. Für Nebennierenrinden-Tumoren gibt es keine spezielle nuklearmedizinische...

Neural Networks. A General Framework for Non-Linear Function Approximation

The focus of this paper is on the neural network modelling approach that has gained increasing recognition in GIScience in recent years. The novelty about neural networks lies in their ability to model non-linear processes with few, if any, a priori assumptions about the nature of the data-generating process. The paper discusses some important issues that are central for successful application development. The scope is limited to feedforward neural networks, the leading example of neural networks. It is argued that failures in applications can usually be attributed to inadequate learning and/or inadequate complexity of the network model. Parameter estimation and a suitably chosen number of hidden units are, thus, of crucial importance for the success of real world neural network applications. The paper views network learning as an optimization problem, reviews two alternative approaches to network learning, and provides insights into current best practice to optimize complexity so to perform well on generalization tasks.

Zur Beurteilung der Krebsgefahr durch Asbest

No abstract is provided for this article.

Future of radionuclide therapy

No abstract is provided for this article.

Neural Networks. A General Framework for Non-Linear Function Approximation

The focus of this paper is on the neural network modeling approach that has gained increasing recognition in GIScience in recent years. The novelty about neural networks lies in their ability to model non-linear processes with few, if any, a priori assumptions about the nature of the data-generating process. The paper discusses some important issues that are central for successful application development. The scope is limited to feedforward neural networks, the leading example of neural networks. It is argued that failures in applications can usually be attributed to inadequate learning and/or inadequate complexity of the network model. Parameter estimation and a suitably chosen number of hidden units are, thus, of crucial importance for the success of real world neural network applications. The paper views network learning as an optimization problem, reviews two alternative approaches to network learning, and provides insights into current best practice to optimize complexity so to perform well on generalization tasks.

Introduction

In this chapter we give an introduction to spatial data analysis, and distinguish it from other forms of data analysis. By spatial data we mean data that contain locational as well as attribute information. We focus on two broad types of spatial data: area data and...

Zum Beitrag: Gemeinsame Handlungsempfehlung (S1-Leitlinie) von DGN, OGNMB und SGNM – Radiosynoviorthese – AWMF-Registriernummer 031-023

Die deutsche, österreichische und schweizerische wissenschaftliche Gesellschaft für Nuklearmedizin legen eine 3-Länder-Leitlinie vor, bei der es sich um eine S1-Leitlinie und damit um eine Handlungsempfehlung handelt [1].

Szintigraphie entzündlicher Prozesse mit radioaktiv markierten monoklonalen Antikörpern

Ein Großteil der Infekte in der Traumatologie ist dem „röntgenologisch erweiterten“ klinischen Blick des Therapeuten zugänglich. Die relativ aufwendigen szintigraphischen Untersuchungsmethoden werden, meist ergänzend, bei...