Parameter estimation and inference with spatial lags and cointegration

Jan Mutl, Leopold Soegner

Research output: Contribution to journalArticle

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)1-39
Number of pages39
JournalEconometric Reviews
DOIs
StateAccepted/In press - Nov 8 2017

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Inference
Credit default swap (CDS) spreads
Cointegration
Parameter estimation
Spatial lag
Methodology
Cross section
Economics
Spatial correlation
Ordinary least squares
Estimator
Closeness
Industry data
Financial markets
Least squares estimator
Dynamic panel data model
Two-stage least squares
Market data

Keywords

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

ASJC Scopus subject areas

  • Economics and Econometrics

Cite this

Parameter estimation and inference with spatial lags and cointegration. / Mutl, Jan; Soegner, Leopold.

In: Econometric Reviews, 08.11.2017, p. 1-39.

Research output: Contribution to journalArticle

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