Spatial vector autoregressions

Michael Beenstock, Daniel Felsenstein

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

A spatial vector autoregressive model (SpVAR) is defined as a VAR which includes spatial as well as temporal lags among a vector of stationary state variables. SpVARs may contain disturbances that are spatially as well as temporally correlated. Although the structural parameters are not fully identified in SpVARs, contemporaneous spatial lag coefficients may be identified by weakly exogenous state variables. Dynamic spatial panel data econometrics is used to estimate SpVARs. The incidental parameter problem is handled by bias correction rather than more popular alternatives such as generalised methods of moments (GMM). The interaction between temporal and spatial stationarity is discussed. The impulse responses for SpVARs are derived, which naturally depend upon the temporal and spatial dynamics of the model. We provide an empirical illustration using annual spatial panel data for Israel. The estimated SpVAR is used to calculate impulse responses between variables, over time, and across space. Finally, weakly exogenous instrumental variables are used to identify contemporaneous spatial lag coefficients.

Original languageAmerican English
Pages (from-to)167-196
Number of pages30
JournalSpatial Economic Analysis
Volume2
Issue number2
DOIs
StatePublished - Jun 2007

Keywords

  • Spatial autocorrelation
  • Spatial econometrics
  • Spatial panel data
  • Vector autoregressions

Fingerprint

Dive into the research topics of 'Spatial vector autoregressions'. Together they form a unique fingerprint.

Cite this