No abstract is provided for this article.
In the recent past economists have placed much emphasis on the role of technological change in the developed economies. Witness the many publications in the field of innovation, long waves and economic dynamics. Especially the present world-wide economic recession...
This paper considers the problem of model uncertainty associated with variable selection and specification of the spatial weight matrix in spatial growth regression models in general and growth regression models based on the matrix exponential spatial specification in particular. A natural solution, supported by formal probabilistic reasoning, is the use of Bayesian model averaging which assigns probabilities on the model space and deals with model uncertainty by mixing over models, using the posterior model probabilities as weights. This paper proposes to adopt Bayesian information criterion model weights since they have computational advantages over fully Bayesian model weights. The approach is illustrated for both identifying model covariates and unveiling spatial structures present in pan-European growth data.
In the last few years research on regional development has increased dramatically. Real-world concerns have - to a certain extent - driven this scientific concern of interest. The field has been given a big boost in particular by the process of European integration and the attempt to understand how this deeper integration will work at the regional level. This volume makes a modest attempt to reconsider the issue of regional development mainly from an European perspective and in the light of the transition of society towards a knowledge-driven economy. It originated from the Thirteenth European Advanced Studies Institute in Regional Science, held in Istanbul, July 2-8, 2000. In producing the book, as friends and colleagues, we have benefited from the possibility of exchange of ideas and experience. We have also received useful assistance from the referees who have offered observations and advice in their written reports. The soundness of their comments has contributed immensely to the quality of the volume. We should, in addition, like to acknowledge the timely manner in which contributing authors have responded to our requests, and their willingness to follow the stringent editorial guidelines.
No abstract is provided for this article.
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.
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)
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.
No abstract is provided for this article.
It is customary for a concluding chapter to summarise the findings presented in the preceding part of the book. In this case it is a somewhat daunting task. The three metropolitan region studies that make up the heart of this volume have covered an enormous amount of...
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.
No abstract is provided for this article.
Spectral pattern recognition deals with classifications that utilize pixel-by-pixel spectral information from satellite imagery. The literature on neural network applications in this area is relatively new, dating back only about six to seven years. The first studies...
