Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/72670
Authors: 
Mutl, Jan
Year of Publication: 
2009
Series/Report no.: 
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 237
Abstract: 
I consider a panel vector-autoregressive model with cross-sectional dependence of the disturbances characterized by a spatial autoregressive process. I propose a three-step estimation procedure. Its first step is an instrumental variable estimation that ignores the spatial correlation. In the second step, the estimated disturbances are used in a multivariate spatial generalized moments estimation to infer the degree of spatial correlation. The final step of the procedure uses transformed data and applies standard techniques for estimation of panel vector-autoregressive models. I compare the small-sample performance of various estimation strategies in a Monte Carlo study.
Subjects: 
spatial PVAR
multivariate dynamic panel data model
spatial GM
spatial Cochrane-Orcutt transformation
constrained maximum likelihood estimation
JEL: 
C13
C31
C33
Document Type: 
Working Paper

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