Parameter estimation and inference with spatial lags and cointegration

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 296

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: dynamic ordinary least squares ; cointegration ; credit risk ; spatial autocorrelation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mutl, Jan ; Sögner, Leopold
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/72691
• Last update: 10.03.2025, 11:41 AM CET