Parameter estimation and inference with spatial lags and cointegration

Jan Mutl, Leopold Sögner

Research output: Contribution to journalArticlepeer-review

Abstract

This article studies dynamic panel data models in which 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 a model with several cointegrating relationships that, unlike in the standard case, 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 under a set of low-level assumptions. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, as well as firm-specific and industry data. We construct the economic space using a “closeness” measure for firms based on input–output matrices. Our estimates show that this particular form of spatial correlation of credit default swap spreads is substantial and highly significant.

Original languageEnglish (US)
Pages (from-to)597-635
Number of pages39
JournalEconometric Reviews
Volume38
Issue number6
DOIs
StatePublished - Jul 3 2019

Keywords

  • Cointegration
  • credit risk
  • dynamic ordinary least squares
  • spatial autocorrelation

ASJC Scopus subject areas

  • Economics and Econometrics

Fingerprint Dive into the research topics of 'Parameter estimation and inference with spatial lags and cointegration'. Together they form a unique fingerprint.

Cite this