A SPATIO-TEMPORAL AUTOREGRESSIVE SEM MODEL FOR REDUCING OMITTED VARIABLE BIAS

Yusep Suparman

Abstract


Structural Equation Modeling (SEM) has been recognized as a powerful analytical tool.
Nevertheless,  SEM  assumes  that  observations  are  independent.  This  assumption
prevents  us  to  apply  SEM  in  spatial  modeling  in  which observations  depend  on  each
other according to their position in a space. Here we propose to formulate a SEM for accommodating  spatial  dependency  among  observations.  Particularly,  we  focus  on
spatio-temporal  autoregressive  model  for  reducing  omitted  variable  bias  in  a  spatial
autoregressive model.

Keywords


SEM; spatio-temporal; autoregressive;

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References


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