gumprecht daniela (5 risultati)

Paperback. Condizione: Brand New. 100 pages. 8.90x5.90x0.40 inches. In Stock.

Taschenbuch. Condizione: Neu. Spatial Methods in Econometrics | Daniela Gumprecht | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783836462891 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gumprecht DanielaDaniela Gumprecht, Dr.rer.soc.oec.: Studies of Statistics at the University of Vienna and the Vienna University of Economics and Business Administration.This work deals with the adequate handling of spatial data .

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work deals with the adequate handling of spatial data in general, and in particular in the framework of economic sciences. An overview of well known methods from the field of spatial statistics and spatial econometrics is given. Furthermore a special class of spatial objects is described, namely objects that are that far apart from all other observations in the dataset, that they are not connected to them anymore. Different treatments of such objects are suggested and their influence on the Moran's I test for spatial autocorrelation is analysed. After this theoretical part some adequate spatial methods are applied to the well-known problem of R&D spillovers. Here the spatial contiguity matrix is based on an economic distance measure instead of the commonly used geographic distances. In the last part, optimal design theory and spatial analysis are combined via a new criterion, which was developed to be able to take a potential spatial dependency of the data points into account. The target audience for this book are statistics students and scientific researchers who are familiar with the standard tools for regression modelling, optimal design theory and statistical inference.