This monograph deals with spatially dependent nonstationary time series
in a way accessible to both time series econometricians wanting to
understand spatial econometics, and spatial econometricians lacking a
grounding in time series analysis. After charting key concepts in both
time series and spatial econometrics, the book discusses how the spatial
connectivity matrix can be estimated using spatial panel data instead of
assuming it to be exogenously fixed. This is followed by a discussion of
spatial nonstationarity in spatial cross-section data, and a full
exposition of non-stationarity in both single and multi-equation
contexts, including the estimation and simulation of spatial vector
autoregression (VAR) models and spatial error correction (ECM) models.
The book reviews the literature on panel unit root tests and panel
cointegration tests for spatially independent data, and for data that
are strongly spatially dependent. It provides for the first time
critical values for panel unit root tests and panel cointegration tests
when the spatial panel data are weakly or spatially dependent.
The volume concludes with a discussion of incorporating strong and weak
spatial dependence in non-stationary panel data models. All discussions
are accompanied by empirical testing based on a spatial panel data of
house prices in Israel.