Hey everyone,
I'm puzzled about a recent result, and I'm wondering if anyone can help explain
it.
I am currently estimating a growth model with many controls by OLS. Looking at
the residual tests I find that the Breusch-Pagan test points the presence of
heteroskedasticity.
Moreover, looking for spatial dependence in the residuals using the Moran's I
test I find that with 3 definitions of spatial weight matrix I cannot reject
the null hp of no spatial dependence while with 3 different spatial weight
matrices I can.
Here the results in details:
Breush-Pagan Test:
BP=66.3478, p-value=0
Moran'I test on regression residuals:
1) W1 is binary matrix with a cut-off=660.8 km (row-standardized):
Observed Moran's I=-0.02938, p-value=/0.57547/
2) W2 is the first-order contiguity matrix(row-standardized):
Observed Moran's I=0.1389, p-value=_0.00021_
3) W3 is the second-order contiguity matrix(row-standardized):
Observed Moran's I=0.0694 , p-value=_0.00724_
4) W4 is the matrix s.t each region has at least one neighbour- max distance
1124.710 km -(row-standardized):
Observed Moran's I=-0.01286, p-value=/0.91883/
5) W5 is the matrix where weights are 1/d_ij^2 with no
cut-off(row-standardized):
Observed Moran's I=0.03239, p-value=_0.00350_
6) W6 is the matrix where weights are 1/exp(2*d_ij) with no
cut-off(row-standardized):
Observed Moran's I=0.06847, p-value=/0.15462/
Therefore, my questions are:
- since I find evidence of heteroskedasticity shouldn't I look for
aheteroskedasticity-robust version of the Moran's I? If yes, is there the
possibility
to implement it with the function "lm.morantest"?
- what should I conclude from such different results using different spatial
weight matrices? It seems that the lower the number of neighbours is the higher
is the
probability of finding spatial effects. In this latter case how can I decide
the "right" matrix? By looking at the AIC in the maximum likelihood?
Thank you very much!
Angela Parenti
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