TITLE:
Modeling Spatial Data Pooled over Time: Schematic Representation and Monte Carlo Evidences
AUTHORS:
Jean Dubé, Diego Legros
KEYWORDS:
Spatio-Temporal Data, Data Generating Process, Spatial Econometric, Weights Matrices, Monte Carlo Experiment
JOURNAL NAME:
Theoretical Economics Letters,
Vol.5 No.1,
February
15,
2015
ABSTRACT: The spatial autocorrelation issue is now well established, and it is
almost impossible to deal with spatial data without considering this reality.
In addition, recent developments have been devoted to developing methods that
deal with spatial autocorrelation in panel data. However, little effort has
been devoted to dealing with spatial data (cross-section) pooled over time.
This paper endeavours to bridge the gap between the theoretical modeling
development and the application based on
spatial data pooled over time. The paper presents a schematic representation of
how spatial links can be expressed, depending on the nature of the
variable, when combining the spatial multidirectional relations and temporal
unidirectional relations. After that, a Monte Carlo experiment is conducted to
establish the impact of applying a usual spatial econometric model to spatial
data pooled over time. The results suggest that neglecting the temporal
dimension of the data generating process can introduce important biases on
autoregressive parameters and thus result in the inaccurate measurement of the indirect and total spatial effect related
to the spatial spillover effect.