Dear all,

I have found different results while using GEODA (spatial lag and error
models) and spdep (lagsarlm and errorsarlm) models for the same data and
same weight matrix.  GEODA indicated strong spatial lag and error dependency
by Moran's I and LR tests and highly significant coefficients of lagged
dependent variable and the lambda. However, in lagsarlm and errorsarlm
models in R I found both Rho and Lambda are insignificant by the LR tests
and also insignificant Moran's Is. Also the magnitude of coefficient
estimate differs for other model variables in these two applications.

What might have caused these differences? Is the parameter estimation by MLE
in GEODA and GLS (except Rho, which perhaps by MLE) in R made this
difference. Why moran's I is significant in one (GEODA) but not in other
(R)? What i am missing here?

Any clue and suggestion would be helpful to find this difference.  Following
is the data description and sample model results:
I have used country based data from 124 countries with some islands on it.
Created a gal file in GEODA and run simultaneous models in GEODA and R.

1.  sample GEODA out put for a model:

DIAGNOSTICS FOR SPATIAL DEPENDENCE
FOR WEIGHT MATRIX : gdpgi07.GAL  (row-standardized weights)
TEST                          MI/DF      VALUE          PROB
Moran's I (error)           0.266382     4.1677032      0.0000308
Lagrange Multiplier (lag)       1       20.7711707      0.0000052
Robust LM (lag)                 1        9.7078520      0.0018348
Lagrange Multiplier (error)     1       13.0279696      0.0003069
Robust LM (error)               1        1.9646508      0.1610168
Lagrange Multiplier (SARMA)     2       22.7358216      0.0000116

sample spatial lag model output for the same model in GEODA:
-----------------------------------------------------------------------
    Variable    Coefficient     Std.Error    z-value      Probability
-----------------------------------------------------------------------
       W_Y     0.5758454     0.05216534       11.03885    0.0000000
    CONSTANT     -43.92029       21.60366      -2.033003    0.0420521
         X1     0.3878955      0.0528817       7.335156    0.0000000
        X2     0.8597154      0.8199795        1.04846    0.2944269
    -----------------------------------------------------------------------

DIAGNOSTICS FOR SPATIAL DEPENDENCE
SPATIAL LAG DEPENDENCE FOR WEIGHT MATRIX : gdpgi07.GAL
TEST                                     DF     VALUE         PROB
Likelihood Ratio Test                    1       37.85142     0.0000000

2. Following is the R results for the same model:

moran.test(Y,gdpgi07.queen,randomisation=FALSE,zero.policy=TRUE
,alternative="two.sided")

Moran's I test under normality

data:  Y
weights: gdpgi07.queen

Moran I statistic standard deviate = -1.1922, p-value = 0.2332
alternative hypothesis: two.sided
sample estimates:
Moran I statistic       Expectation          Variance
     -0.103175115      -0.008849558       0.006259578


       Global Moran's I for regression residuals

data:
model: lm(formula = Y ~X1 + X2 +.......)
weights: gdpgi07.queen

Moran I statistic standard deviate = -0.7751, p-value = 0.4383
alternative hypothesis: two.sided
sample estimates:
Observed Moran's I        Expectation           Variance
      -0.067836924       -0.006025453        0.006359538

Spatial lag model results:
model.lag<-lagsarlm(Y~X1+X2+......................,data=gdpgi,gdpgi07.queen,
zero.policy=TRUE)
summary(amph1.lag)
Type: lag
Regions with no neighbours included:
 199 98 183 216 99 157 105 143 118 12
Coefficients: (asymptotic standard errors)
               Estimate  Std. Error z value  Pr(>|z|)
(Intercept) -59.5478780  25.4744792 -2.3376   0.01941
X1           0.4084015   0.0611086  6.6832 2.338e-11
X2          2.2414218   0.9592832  2.3366   0.01946

Rho: -0.056746 LR test value: 0.51535 p-value: 0.47283

thank you in advance.

Ram

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