TY - JOUR
T1 - SPATIAL DEPENDENCE AND SPATIAL STRUCTURAL INSTABILITY IN APPLIED REGRESSION ANALYSIS
AU - Anselin, Luc
PY - 1990/5
Y1 - 1990/5
N2 - The stability of regression coefficients over the observation set (“regional homogeneity”) is typically assessed by means of a Chow test or within a seemingly unrelated regression (SUR) framework. When spatial error autocorrelation is present in cross‐sectional equations the traditional tests are no longer applicable. I evaluate this both in formal terms as well as empirically. I introduce a taxonomy of spatial effects in models for structural instability, and discuss its implication for testing. I compare the performance of traditional tests, robust approaches, maximum‐likelihood procedures and pretest techniques by means of a series of simple Monte Carlo experiments.
AB - The stability of regression coefficients over the observation set (“regional homogeneity”) is typically assessed by means of a Chow test or within a seemingly unrelated regression (SUR) framework. When spatial error autocorrelation is present in cross‐sectional equations the traditional tests are no longer applicable. I evaluate this both in formal terms as well as empirically. I introduce a taxonomy of spatial effects in models for structural instability, and discuss its implication for testing. I compare the performance of traditional tests, robust approaches, maximum‐likelihood procedures and pretest techniques by means of a series of simple Monte Carlo experiments.
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U2 - 10.1111/j.1467-9787.1990.tb00092.x
DO - 10.1111/j.1467-9787.1990.tb00092.x
M3 - Article
AN - SCOPUS:0025671386
VL - 30
SP - 185
EP - 207
JO - Journal of Regional Science
JF - Journal of Regional Science
SN - 0022-4146
IS - 2
ER -