Unit Root and Cointegration Tests in Spatial Cross-Section Data

Michael Beenstock and Daniel Felsenstein
Additional contact information
Michael Beenstock: Hebrew University of Jerusalem
Daniel Felsenstein: Hebrew University of Jerusalem

Chapter Chapter 5 in The Econometric Analysis of Non-Stationary Spatial Panel Data, 2019, pp 97-127 from Springer

Abstract: Abstract Although nonstationarity arises in time series data, it may also arise in spatial cross-section data. A definition of spatial nonstationarity is provided in which spatial impulse responses do not weaken with distance. Hence, remote shocks have the same effect on outcomes in a location, as do shocks in its immediate neighbors. This definition implies that the SAR coefficients tends to unity. Analytical methods are used to derive spatial impulse responses when space is lateral (east–west), and simulation methods are used when space is bilateral (east–west, north–south). A lattice based spatial unit root test is proposed to test for spatial nonstationarity, and its critical values are calculated using Monte Carlo simulation methods. We show that these critical values depend on the specification of W. Since space is finite, the asymptotic properties of spatial unit root tests are undefined. Specifically, because finite space has edges, unit roots that would arise if space was infinite, do not arise when space is finite, because locations on the edge of space are less spatially connected than their counterparts in the epicenter of space. We use simulation methods to show how edge effects weaken spatial unit roots. If spatial cross-section data are nonstationary, spatial cross-section regression parameter estimates may be spurious or nonsense. A spatial cointegration test is proposed in which, under the null, the variables concerned are not spatially cointegrated, in which event the parameter estimates are genuine. They are spatially cointegrated if the residual errors are spatially stationary. If they are spatially cointegrated, their parameter estimates are super-consistent. Monte Carlo methods are used to calculate critical values to test for spatial cointegration in cross-section data.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:adspcp:978-3-030-03614-0_5

Ordering information: This item can be ordered from
http://www.springer.com/9783030036140

DOI: 10.1007/978-3-030-03614-0_5

Access Statistics for this chapter

More chapters in Advances in Spatial Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().