Abstract
We consider a class of spatio-temporal models which extend popular econometric spatial autoregressive panel data models by allowing the scalar coefficients for each location (or panel) different from each other. To overcome the innate endogeneity, we propose a generalized Yule–Walker estimation method which applies the least squares estimation to a Yule–Walker equation. The asymptotic theory is developed under the setting that both the sample size and the number of locations (or panels) tend to infinity under a general setting for stationary and α-mixing processes, which includes spatial autoregressive panel data models driven by i.i.d. innovations as special cases. The proposed methods are illustrated using both simulated and real data.
Highlights
The class of spatial autoregressive (SAR) models is introduced to model cross sectional dependence of different economic individuals at different locations (Cliff and Ord, 1973)
Yu et al (2008, 2012) investigate the asymptotic properties when both the number of locations and the length of time series tend to infinity for both the stable case and spatial cointegration case, and show that QMLE is consistent
We develop the asymptotic properties under a general setting for stationary and αmixing processes, which includes the spatial autoregressive panel data models driven by i.i.d. innovations as special cases
Summary
The class of spatial autoregressive (SAR) models is introduced to model cross sectional dependence of different economic individuals at different locations (Cliff and Ord, 1973). More recent developments extend SAR models to spatial dynamic panel data (SDPD) models, i.e. adding time lagged terms to account for serial correlations across different locations. Both estimation method and asymptotic analysis need to be adapted under this new setting. Yu et al (2008, 2012) investigate the asymptotic properties when both the number of locations and the length of time series tend to infinity for both the stable case and spatial cointegration case, and show that QMLE is consistent. We develop the asymptotic properties under a general setting for stationary and αmixing processes, which includes the spatial autoregressive panel data models driven by i.i.d. innovations as special cases.
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