Parameter estimation and inference with spatial lags and cointegration

Mutl, Jan and Sögner, LeopoldORCID: (2019) Parameter estimation and inference with spatial lags and cointegration. Econometric Reviews, 38 (6), pp. 597-635.

Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (862kB) | Preview


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.

Item Type: Article in Academic Journal
Research Units: Macroeconomics and Economic Policy
Date Deposited: 10 Jul 2019 13:43
Last Modified: 12 Jul 2019 09:06
DOI: 10.1080/07474938.2017.1382803
ISSN: 0747-4938

Actions (login required)

View Item View Item