In this paper, a Poisson gravity model is introduced that incorporates spatial dependence of the explained variable without relying on restrictive distributional assumptions of the underlying data generating process. The model comprises a spatially filtered component including the origin, destination and origin-destination specific variables and a spatial residual variable that captures origin- and destination-based spatial autocorrelation. We derive a 2-stage nonlinear least squares estimator that is heteroscedasticity-robust and, thus, controls for the problem of over- or under-dispersion that often is present in the empirical analysis of discrete data or, in the case of over-dispersion, if spatial autocorrelation is present. This estimator can be shown to have desirable properties for different distributional assumptions, like the observed flows or (spatially) filtered component being either Poisson or Negative Binomial. In our spatial autoregressive model specification, the resulting parameter estimates can be interpreted as the implied total impact effects defined as the sum of direct and indirect spatial feedback effects. Monte Carlo results indicate marginal finite sample biases in the mean and standard deviation of the parameter estimates and convergence to the true parameter values as the sample size increases. In addition, the paper illustrates the model by analysing patent citation flows data across European regions.
This paper investigates the impact of knowledge capital stocks on total factor productivity 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 total factor productivity (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 labour 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 regional knowledge capital stocks for N=203 regions over the 1997- 2002 time period. In estimating the effects we implement a spatial panel data model that controls for the spatial autocorrelation due to neighbouring regions and the 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 the discussion, by showing that productivity
This paper uses data for 255 NUTS-2 European regions over the period 1995-2003 to test the relative explanatory performance of two important rival theories seeking to explain variations in the level of economic development across regions, namely the neoclassical model originating from the work of Solow (1956) and the so-called Wage Equation, which is one of a set of simultaneous equations consistent with the short-run equilibrium of new economic geography (NEG) theory, as described by Fujita, Krugman and Venables (1999). The rivals are non-nested, so that testing is accomplished both by fitting the reduced form models individually and by simply combining the two rivals to create a composite model in an attempt to identify the dominant theory. We use different estimators for the resulting panel data model to account variously for interregional heterogeneity, endogeneity, and temporal and spatial dependence, including maximum likelihood with and without fixed effects, two stage least squares and feasible generalised spatial two stage least squares plus GMM; also most of these models embody a spatial autoregressive error process. These show that the estimated NEG model parameters correspond to theoretical expectation, whereas the parameter estimates derived from the neoclassical model reduced form are sometimes insignificant or take on counterintuitive signs. This casts doubt on the appropriateness of neoclassical theory as a basis for explaining cross-regional variation in economic development in Europe, whereas NEG theory seems to hold in the face of competition from its rival.
Adrenal venous aldosterone determinations, adrenal phlebography, adrenal scintigraphy, and computed tomography (CT) are used in differentiating unilateral adrenal adenomas and carcinomas from bilateral adrenal hyperplasia in patients with primary aldosteronism. Due...
Club-convergence analysis provides a more realistic and detailed picture about regional income growth than traditional convergence analysis. This paper presents a spatial econometric framework for club-convergence testing that relates the concept of club-convergence to the notion of spatial heterogeneity. The study provides evidence for the club-convergence hypothesis in cross-regional growth dynamics from a pan-European perspective. The conclusions are threefold. First, we reject the standard Barro-style regression model which underlies most empirical work on regional income convergence, in favour of a two regime [club] alternative in which different regional economies obey different linear regressions when grouped by means of Getis and Ord's (1992) local clustering technique. Second, the results point to a heterogeneous pattern in the pan-European convergence process. Heterogeneity appears in both the convergence rate and the steady-state level. But, third, the study also reveals that spatial error dependence introduces an important bias in our perception of the club-convergence and shows that neglection of this bias would give rise to misleading conclusions.
Außer typischerVeronica chamaedrys L. findet sich in Österreich eine Sippe mit deutlich dichterer und kürzerer Behaarung der Sepalen, meist
Questions of convergence have received increasing attention in recent years, in light of the pressure for greater integration and enlargement of the European Union [EU] to countries in Central and Eastern Europe [CEE]. This paper looks at the evidence for convergence of per capita income between regions in Europe in the second half of the 1990s, when economic recovery in CEE gathered pace. The analysis is based on the simplest of the models, the unconditional s-convergence model and shows that the classical test methodology is ill-designed due to two reasons. First, it cannot identify groupings of regional economies that are converging. Second, it neglects spatial effects that represent interregional interactions and spatial spillovers. The paper suggests a much richer and theoretically more satisfactory approach that is in line with both the notions of club convergence and spatial dependence, and reflects recent developments in spatial econometrics. The two-club spatial error convergence model with groupwise heteroskedasticity is found to be most appropriate for the data at hand. Two empirical key findings are worthwhile to note. The first is that the data provide much support for unconditional beta-convergence in Europe. The second is that the usual convergence conclusions hold. But they do so for reasons that are not revealed by the classical test equation that is typical in mainstream economics literature.
Geographical Information Systems (GIS) are capable of acquiring spatially indexed data from a variety of sources, changing the data into useful formats, st
Part A Housing market models - structure and operation of housing markets. Part B Housing consumption and demand. Part C Housing choice and residential mobility. Part D Job search theory. Part E Labour supply and demand. Part F Spatial labour market adjustment.
The focus in this paper is on knowledge spillovers between hightechnology firms in Europe, as captured by patent citations. The European coverage is given by patent applications at the European Patent Office (EPO) that are assigned to high-technology firms located in Europe. By following the paper trail left by citations between high-technology patents we adopt a case–control matching approach to test the extent of localisation of knowledge spillovers at two geographic levels, the region and the country level. This approach views a finding of disproportionate co-location of patent citations relative to co-located control patents as evidence of localised knowledge spillovers. To disentangle border from geographic distance effects the paper adopts a Poisson spatial interaction modelling perspective. The findings of the study not only indicate that localisation of knowledge spillovers exists, but also that national border effects are more important than geographical distance effects. Thus, knowledge flows within European countries more easily than across. Not only geography matters, but also technological proximity. Interregional knowledge flows are industry specific and occur most often between regions located close to each other in technological space.
No abstract is provided for this article.
This series is dedicated to reporting our recent research in spatial science in general and economic geography & geoinformatics in particular. It contains scientific studies focusing on spatial phenomena, utilizing theoretical frameworks, analytical methods and empirical procedures specifically designed for spatial analysis. The aim is to present the research at the Department to an informed readership in universities, research organizations and policy-making institutions throughout the world. The type of materials considered for publication in the series includes interim reports presenting work in progress and papers which have been submitted for publication elsewhere. (authors' abstract)
Die Wirkung ionisierender Strahlen im Niedrigdosenbereich mit den möglichen Risiken von Spätschäden wird derzeit noch kontrovers diskutiert. Verschiedene Ansätze und Studien zu diesem Problemkreis wurden von Experten unterschiedlicher Fachrichtungen - der Strahlenbiologie, Nuklearmedizin und Physik - im Laufe des Symposiums vorgestellt. Die Diskussion betraf vor allem genetische Schäden, die sich auf Grund tierexperimenteller Daten belegen lassen sowie Untersuchungen zu kanzerogenen und teratogenen Effekten von Niedrigstdosisstrahlen, vor allem durch natürliche Grundstrahlung oder durch Exponierung in der Strahlenmedizin. Von besonderem Interesse sind die Ergebnisse der Neubewertung des Strahlenkrebsrisikos von Überlebenden der Atombombenexplosionen in Japan. Messungen und Beurteilungen der Gesundheitsrisiken im Zusammenhang mit der Reaktorkatastrophe von Tschernobyl bilden die Grundlage für weitere Diskussionen über Gefahren und Nutzen der Kernenergie.
No abstract is provided for this article.
Leaming in neural networks has attracted considerable interest in recent years. Our focus is on learning in single hidden layer feedforward networks which is posed as a search in the network parameter space for a network that minimizes an additive error function of statistically independent examples. In this contribution, we review first the class of single hidden layer feedforward networks and characterize the learning process in such networks from a statistical point of view. Then we describe the backpropagation procedure, the leading case of gradient descent learning algorithms for the class of networks considered here, as well as an efficient heuristic modification. Finally, we analyse the applicability of these learning methods to the problem of predicting interregional telecommunication flows. Particular emphasis is laid on the engineering judgment, first, in choosing appropriate values for the tunable parameters, second, on the decision whether to train the network by epoch or by pattern (random approximation), and, third, on the overfitting problem. In addition, the analysis shows that the neural network model whether using either epoch-based or pattern-based stochastic approximation outperforms the classical regression approach to modelling telecommunication flows. (authors' abstract)
