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Parameter Estimation and Inference with Spatial Lags and Cointegration

Author

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  • Mutl, Jan

    (EBS Business School, Wiesbaden, Germany)

  • Sögner, Leopold

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

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.

Suggested Citation

  • Mutl, Jan & Sögner, Leopold, 2013. "Parameter Estimation and Inference with Spatial Lags and Cointegration," Economics Series 296, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:296
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    File URL: https://irihs.ihs.ac.at/id/eprint/2201
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    1. Jan Mutl & Leopold Sögner, 2019. "Parameter estimation and inference with spatial lags and cointegration," Econometric Reviews, Taylor & Francis Journals, vol. 38(6), pages 597-635, July.

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    More about this item

    Keywords

    Dynamic ordinary least squares; cointegration; credit risk; spatial autocorrelation;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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