Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/72691
Authors: 
Mutl, Jan
Sögner, Leopold
Year of Publication: 
2013
Series/Report no.: 
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 296
Abstract: 
We study dynamic panel data models where the long run outcome for a particular cross-section is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies several cointegrating relationships that are nonlinear in the coefficients to be estimated. Assuming that the weights are exogenously given, we extend the dynamic ordinary least squares methodology and provide a dynamic two-stage least squares estimator. We derive the large sample properties of our proposed estimator and investigate its small sample distribution in a simulation study. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, firm specific and industry data. A closeness measure for firms is based on input-output matrices. Our estimates show that this particular form of spatial correlation of credit default spreads is substantial and highly significant.
Subjects: 
dynamic ordinary least squares
cointegration
credit risk
spatial autocorrelation
JEL: 
C31
C32
Document Type: 
Working Paper

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