Hello,
Thanks for your helpful comments. I am working on a study about the impact of institution quality on bilateral trade among MENA countries. I have only MENA countries in the original and destination observations. I have this code
ppmlhdfe t ldist col45 comlang_off contig lgdp_o lgdp_d lpop_o lpop_d fta_wto linqulo linquld, absorb(i.imp#c.year i.exp#c.year imp#exp) cluster(imp#exp)
However, I got these results:
HDFE PPML regression No. of obs = 13,485
Absorbing 3 HDFE groups Residual df = 878
Statistics robust to heteroskedasticity Wald chi2(10) = 352.78
Deviance = 741772633.3 Prob > chi2 = 0.0000
Log pseudolikelihood = -370959484.1 Pseudo R2 = 0.9444
Number of clusters (imp#exp)= 879
(Std. err. adjusted for 879 clusters in imp#exp)
Robust
t Coefficient std. err. z P>z [95% conf. interval]
ldist .1130914 .519204 0.22 0.828 -.9045298 1.130713
col45 0 (omitted)
comlang_off .8465815 365.7479 0.00 0.998 -716.0061 717.6993
contig -1.227287 447.2448 -0.00 0.998 -877.8111 875.3565
lgdp_o .5951616 .1206984 4.93 0.000 .358597 .8317261
lgdp_d .7417076 .0944459 7.85 0.000 .556597 .9268182
lpop_o -.0014878 .2368235 -0.01 0.995 -.4656533 .4626777
lpop_d .063504 .2677218 0.24 0.813 -.4612211 .5882291
fta_wto .286897 .1082046 2.65 0.008 .0748199 .4989741
linqulo .2195042 .3682029 0.60 0.551 -.5021602 .9411686
linquld -.3115027 .1948589 -1.60 0.110 -.6934191 .0704136
_cons -12.54079 188.1845 -0.07 0.947 -381.3756 356.294
Absorbed degrees of freedom:
Absorbed FE Categories - Redundant = Num. Coefs
-
imp#c.year 31 0 31 ?
exp#c.year 31 0 31 ?
imp#exp 879 879 0 *
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computationI am trying to capture the importer and exporter fixed effect. Do my results look OK?
Thanks for your helpful comments. I am working on a study about the impact of institution quality on bilateral trade among MENA countries. I have only MENA countries in the original and destination observations. I have this code
ppmlhdfe t ldist col45 comlang_off contig lgdp_o lgdp_d lpop_o lpop_d fta_wto linqulo linquld, absorb(i.imp#c.year i.exp#c.year imp#exp) cluster(imp#exp)
However, I got these results:
HDFE PPML regression No. of obs = 13,485
Absorbing 3 HDFE groups Residual df = 878
Statistics robust to heteroskedasticity Wald chi2(10) = 352.78
Deviance = 741772633.3 Prob > chi2 = 0.0000
Log pseudolikelihood = -370959484.1 Pseudo R2 = 0.9444
Number of clusters (imp#exp)= 879
(Std. err. adjusted for 879 clusters in imp#exp)
Robust
t Coefficient std. err. z P>z [95% conf. interval]
ldist .1130914 .519204 0.22 0.828 -.9045298 1.130713
col45 0 (omitted)
comlang_off .8465815 365.7479 0.00 0.998 -716.0061 717.6993
contig -1.227287 447.2448 -0.00 0.998 -877.8111 875.3565
lgdp_o .5951616 .1206984 4.93 0.000 .358597 .8317261
lgdp_d .7417076 .0944459 7.85 0.000 .556597 .9268182
lpop_o -.0014878 .2368235 -0.01 0.995 -.4656533 .4626777
lpop_d .063504 .2677218 0.24 0.813 -.4612211 .5882291
fta_wto .286897 .1082046 2.65 0.008 .0748199 .4989741
linqulo .2195042 .3682029 0.60 0.551 -.5021602 .9411686
linquld -.3115027 .1948589 -1.60 0.110 -.6934191 .0704136
_cons -12.54079 188.1845 -0.07 0.947 -381.3756 356.294
Absorbed degrees of freedom:
Absorbed FE Categories - Redundant = Num. Coefs
-
imp#c.year 31 0 31 ?
exp#c.year 31 0 31 ?
imp#exp 879 879 0 *
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computationI am trying to capture the importer and exporter fixed effect. Do my results look OK?
