Please use this identifier to cite or link to this item:
Arnold, Matthias
Year of Publication: 
Series/Report no.: 
Technical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 2007,25
This paper suggests an improved GMM estimator for the autoregressive parameter of a spatial autoregressive error model by taking into account that unobservable regression disturbances are di.erent from observable regression residuals. Although this di.erence decreases in large samples, it is important in small samples. Monte Carlo simu­lations show that the bias can be reduced by 65 - 80% compared to a GMM estimator that neglects the difference between disturbances and residuals. The mean squared error is smaller, too.
GMM estimation
spatial autoregression
regression residuals
Document Type: 
Working Paper

Files in This Item:
141.78 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.