Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/72691 
Erscheinungsjahr: 
2013
Schriftenreihe/Nr.: 
Reihe Ökonomie / Economics Series No. 296
Verlag: 
Institute for Advanced Studies (IHS), Vienna
Zusammenfassung: 
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.
Schlagwörter: 
dynamic ordinary least squares
cointegration
credit risk
spatial autocorrelation
JEL: 
C31
C32
Dokumentart: 
Working Paper

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