Hi,

I have a panel GDP data for over 200 countries for the years 1990 to 2018, which I am reducing to 6 different sub-samples. I am estimating the effect of 9 year dummies in each of these subsamples.

I am attempting to show my regression estimates using suest, so that I can then make a linear combination of these estimates using lincom, which will amount to a pooled average. However, I have noticed that suest doesnt allow for clustering by country in the original regressions, and requires me to cluster by country when I use suest. I suspect this is not the equivalent. I was wondering if there is an alternative way to combine these estimates so that I can obtain the pooled average over the 6 sub-samples.

Additionally, I would like to weight the linear combination by the standard error of the regression estimates. I am not aware how to do this without entering the values manually.

The ** indicate the clustering I believe is necessary but am stopped from doing by suest.

Part of my code is shown below

Code:
*US 
replace subsamp = 6 if inlist(Country, 207, 41,27,134)
balancetable Host Population GDP OwnGDPGROWTH GDPCAP GDPCapGrowth using "Documents\Masters\Thesis\Data\Country Panel\Output\BalancingUS2.xls" if year<=1997 & subsamp ==6 , pvalues   ctitles(Non-Host, Host, Difference(p)) replace
reg OwnGDPGROWTH Pre4 Pre3 Pre2 Pre1 t Post1 Post2 Post3 Post4 i.year ib27.Country if subsamp == 6 , **cluster(CountryName)**
eststo US
outreg2 using "Documents\Masters\Thesis\Data\Country Panel\Output\Regressions\PoolingSample2.xml" , append excel word dec(3) ctitle(USA) nocons keep(Pre4 Pre3 Pre2 Pre1 t Post1 Post2 Post3 Post4) addtext(Country FE, Y, Year FE, Y) title(Countries: FB Pooling)


suest Bra SA Ger SKJap Fra US , cluster(CountryName)

*Sum- Pre 4 pooled
lincom (([Bra_mean]Pre4) + ([SA_mean]Pre4) + ([Ger_mean]Pre4)  + ([SKJap_mean]Pre4) + ([Fra_mean]Pre4)  +([US_mean]Pre4)  )/ 6
*Pre3 estimate .......