Is spurious behaviour an issue for two independent stationary spatial autoregressive SAR(1) processes?
Spurious regression occurs when two independent stationary or nonstationary time series are found to be correlated. Spurious behaviour is also detected in spatial data. Using a Monte Carlo analysis, this study examines the spurious phenomenon for two independent stationary spatial autoregressive processes of order one, that is, SAR(1), and it finds that when spatial econometric models are estimated, as suggested by the LM specification tests, the spurious behaviour is not detected nor the presence of spatially autocorrelated errors.
FOR TWO INDEPENDENT STATIONARY AR ( 1 ) PROCESSES ?
When time series data is used in econometrics serially correlated errors are most likely to appear. Autocorrelation will also be detected in regression analysis as an indication of a false specification between two variables. This study examines the problem of serially correlated errors in the context of spurious regression showing evidence of removing the presence of this phenomenon both theoretically as well as empirically by applying the Cochrane-Orcutt procedure.
Modelling spatial processes : the identification and analysis of spatial relationships in regression residuals by means of Moran's I : with 32 figures and 8 talbes
The regression model.- The specification of spatial relations.- Gaussian spatial processes.- The characteristic function.- Numerical evaluation of Imhof's formula.- The exact distribution of Moran's I.- The shape of Moran's I exact distribution.- The moments of Moran's I.- The basic ecological model and spatial setting.- A spatial analysis of the cancer data.- Conclusions.