Spatial Methods in Econometrics. An Application to R&D Spillovers.

Gumprecht, Daniela (2005) Spatial Methods in Econometrics. An Application to R&D Spillovers. Research Report Series / Department of Statistics and Mathematics, 26. Department of Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna.


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In this paper I will give a brief and general overview of the characteristics of spatial data, why it is useful to use such data and how to use the information included in spatial data. The first question to be answered is: how to detect spatial dependency and spatial autocorrelation in data? Such effects can for instance be found by calculating Moran's I, which is a measure for spatial autocorrelation. The Moran's I is also the basis for a test for spatial autocorrelation (Moran's test). Once we found some spatial structure we can use special models and estimation techniques. There are two famous spatial processes, the SAR- (spatial autoregressive) and the SMA- (spatial moving average process) process, which are used to model spatial effects. For estimation of spatial regression models there are mainly two different possibilities, the first one is called spatial filtering, where the spatial effect is filtered out and standard techniques are used, the second one is spatial two stage least square estimation. Finally there are some results of a spatial analysis of R&D spillovers data (for a panel dataset with 22 countries and 20 years) shown. (author's abstract)

Item Type: Paper
Keywords: spatial dependency and autocorrelation / Moran's I / SAR- and SMA process / spatial filtering / S2SLS
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Repository Administrator
Date Deposited: 22 Dec 2005 15:28
Last Modified: 22 Oct 2019 00:41


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