Dear all,

I have conducted a SDM model using xsmle in Stata and obtained the following results:


SDM with spatial fixed-effects Number of obs = 4734

Group variable: regid36 Number of groups = 263
Time variable: year Panel length = 18

R-sq: within = 0.1923
between = 0.3338
overall = 0.0602

Mean of fixed-effects = 0.3286

Log-likelihood = 10715.9469
------------------------------------------------------------------------------
gdppcgr | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Main |
laglgdppc | -.1611488 .0078934 -20.42 0.000 -.1766196 -.1456781
linvr | .0027371 .0030948 0.88 0.376 -.0033286 .0088028
lpopgr | -.033131 .0033712 -9.83 0.000 -.0397385 -.0265235
wgipca | .0116851 .0016333 7.15 0.000 .0084838 .0148864
lefpayr | -.0052668 .1060317 -0.05 0.960 -.2130852 .2025515
wgilefp | -.0188772 .0426143 -0.44 0.658 -.1023997 .0646454
-------------+----------------------------------------------------------------
Wx |
laglgdppc | .1314624 .0093483 14.06 0.000 .11314 .1497847
linvr | .0219235 .0055082 3.98 0.000 .0111276 .0327195
lpopgr | .0339427 .0070075 4.84 0.000 .0202083 .0476771
wgipca | -.0097747 .002402 -4.07 0.000 -.0144824 -.0050669
lefpayr | .2987033 .1790174 1.67 0.095 -.0521644 .6495709
wgilefp | .0781617 .0630748 1.24 0.215 -.0454626 .201786
-------------+----------------------------------------------------------------
Spatial |
rho | .7397675 .0133931 55.24 0.000 .7135175 .7660174
-------------+----------------------------------------------------------------
Variance |
sigma2_e | .0005853 .0000122 47.83 0.000 .0005613 .0006093
-------------+----------------------------------------------------------------
LR_Direct |
laglgdppc | -.1591031 .0079131 -20.11 0.000 -.1746125 -.1435938
linvr | .0059994 .0029658 2.02 0.043 .0001865 .0118123
lpopgr | -.0313583 .0033271 -9.43 0.000 -.0378792 -.0248374
wgipca | .0114947 .00153 7.51 0.000 .0084958 .0144935
lefpayr | .0380498 .0998539 0.38 0.703 -.1576602 .2337598
wgilefp | -.0077706 .0400321 -0.19 0.846 -.086232 .0706907
-------------+----------------------------------------------------------------
LR_Indirect |
laglgdppc | .0455653 .0202692 2.25 0.025 .0058383 .0852923
linvr | .0878984 .0159565 5.51 0.000 .0566242 .1191726
lpopgr | .0370957 .0236823 1.57 0.117 -.0093207 .0835121
wgipca | -.0039658 .0058497 -0.68 0.498 -.015431 .0074995
lefpayr | 1.064335 .5218013 2.04 0.041 .0416234 2.087047
wgilefp | .2333985 .1701132 1.37 0.170 -.1000172 .5668142
-------------+----------------------------------------------------------------
LR_Total |
laglgdppc | -.1135378 .020262 -5.60 0.000 -.1532506 -.0738251
linvr | .0938978 .0164368 5.71 0.000 .0616822 .1261133
lpopgr | .0057374 .0246923 0.23 0.816 -.0426586 .0541334
wgipca | .0075289 .0055652 1.35 0.176 -.0033786 .0184364
lefpayr | 1.102385 .5294337 2.08 0.037 .0647141 2.140056
wgilefp | .2256279 .1705046 1.32 0.186 -.108555 .5598107
------------------------------------------------------------------------------

My question, whether in the paper I should report the Main and WX part of the regression or the long-run direct, indirect and total results.


Best,

Martin Hulenyi