Parameter Estimation and Inference with Spatial Lags and Cointegration

Mutl, Jan and Sögner, Leopold (May 2013) Parameter Estimation and Inference with Spatial Lags and Cointegration. IHS Economics Series 296


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Abstract or Table of Contents

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 cointegratingrelationships 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 industrydata. 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.;

Item Type: IHS Series
Keywords: 'Dynamic ordinary least squares' 'Cointegration' 'Credit risk' 'Spatial autocorrelation'
Classification Codes (e.g. JEL): C31, C32
Status: Published
Date Deposited: 26 Sep 2014 10:39
Last Modified: 01 Mar 2019 06:15

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