Hello,

I have question about the interpretation of the results of my regression.
I'm estimating the convergence between European regions with spatial
regimes. I have first run an OLS regression for the whole set of regions and
I have found evidence of residual autocorrelation through the LMerr and
LMlag tests.
This was the result:

LMerr = 5.4816, df = 1, p-value = 0.01922

RLMerr = 0.2422, df = 1, p-value = 0.6226

LMlag = 6.3184, df = 1, p-value = 0.01195


RLMlag = 1.079, df = 1, p-value = 0.2989

SARMA = 6.5606, df = 2, p-value = 0.03762

I'm modelling spatial heterogeneity through the estimation of two separate
models for Objective 1 regions and the rest of the regions. According to the
results of the LM tests I have decided to use a spatial lag model (is this
correct in your opinion?). I did so because I find that the LMlag test is a
little more significant than the LMerr... which is the decision rule in this
case (both LM test significant and both RLM test non significant)?

After running the first ML regression I see that there is another LM test
computed at the end of the results of the regression:

Log likelihood: 176.7501 for lag model
ML residual variance (sigma squared): 4.7437e-05, (sigma: 0.0068875)
Number of observations: 50
Number of parameters estimated: 7
AIC: -339.5, (AIC for lm: -334.18)
*LM test for residual autocorrelation
test value: 3.4731 p-value: 0.062373 *

Which is the interpretation to give to it? Is this relative to a spatial
error or to a spatial lag?

Thank you very much for your help!
Annachiara Saguatti

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