Parameter Estimation and Inference with Spatial Lags and Cointegration

Jan Mutl and Leopold Sögner
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Leopold Sögner: Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria

No 296, Economics Series from Institute for Advanced Studies

Abstract: We study dynamic panel data models where the long run outcome for a particular crosssection 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 inputoutput matrices. Our estimates show that this particular form of spatial correlation of credit default spreads is substantial and highly significant.

Keywords: Dynamic ordinary least squares; cointegration; credit risk; spatial autocorrelation (search for similar items in EconPapers)
JEL-codes: C31 C32 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2013-05
New Economics Papers: this item is included in nep-ecm and nep-ets
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https://irihs.ihs.ac.at/id/eprint/2201 First version, 2013 (application/pdf)

Related works:
Journal Article: Parameter estimation and inference with spatial lags and cointegration (2019)
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