Spatial and Spatio-Temporal Error Correction Networks and Common Correlated Effects

Publication date: 19 Feb 2021 | Publication type: NIESR Discussion Paper | NIESR Author(s): Holly, S; Bhattacharjee, A | External Author(s): Ditzen, J | JEL Classification: C21, C22, C23, R3. | NIESR Discussion Paper Number: 526

Abstract

We provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes for spatial or network dynamics, both of which can be expressed in terms of spatial weights matrices. The first captures strong cross-sectional dependence, so that a spatial difference, suitably defined, is weakly cross-section dependent (granular) but can be nonstationary. The second is a conventional weights matrix that captures short-run spatio-temporal dynamics as stationary and granular processes. In large samples, cross-section averages serve the first purpose and we propose the mean group, common correlated effects estimator together with multiple testing of cross-correlations to provide the short-run spatial weights. We apply this model to the 324 local authorities of England, and show that our approach is useful for modelling weak and strong cross-section dependence, together with partial adjustments to two long-run equilibrium relationships and short-run spatio-temporal dynamics. This exercise provides new insights on the (spatial) long run relationship between house prices and income in the UK.

Keyword tags: 
Spatio-temporal dynamics; Error Correction Models; Weak and strong cross sectional dependence.