[Mar Teeno]

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[Mar Teeno]

Shailaja:
-xtreg, mle and -mixed- (that replaced -xtmixed- from Stata 13 onward. BTW: if you're using a Stata release older than 17 you're kindly requested to mention it in your posts, for reasond that are well explained in the FAQ. Thanks) are close cousins.
Therefore, if -re- is the way to go and you switch to -fe-, other things being equal your estimates will still be consistent but inefficient, because the -fe- estimator calculates the standard errors based on within-panel variation only, whereas the -re- one uses a mix of between- and within-panel variation for the same goal.
In fact, I do not see how -fe- could ever be a reliable test for the robustness (BTW robustness vs. what?) of your -mixed- regression.
As far as your last question is concerned (and provided that I would not sponsor going -fe- there), I would type:

xtreg Consumption Literacy i.Drought##i.member i.Flood##i.member c.Price##i.member i.Round , fe vce(cluster panelid)

Finally, I would be much more concerned about endogeneity (other things being equal, more educated individuals may have a higher level of consumption, but a lower level of consumption may mean that a share of the household's disposable income is devoted to the education of the family members (a sort of reverse causation-led endogeneity); disposable income may affect both the regressand (consumption; other things being equal, a higher disposable income may mean higher level of consumption) and one of the predictor (literacy; other things being equal, a higher disposable income may mean a higher level of literacy shared by all the family members; the latetr is an example of latent variable-led endogeneity) and the correctness of the functional form of the regressand.

Dear Statalists,
I am working on the following Error Correction Model with interaction between independent variables (var_f are first difference variables var_lag are lagged variables).

 xtreg socexp_ouh_f socexp_ouh_lag c.openc_f##c.tot_gen_f c.openc_lag##c.tot_gen_lag unemp_f unemp_lag ///
de_ind_f de_ind_lag age_dep_f age_dep_lag infla_f infla_lag c.year, fe vce(cluster isocode)

.
I fear, as suggested by Carlo Lazzaro in this post, that my model may be affected by endogeneity (in particular the variable tot_gen is the one that worries me the most since it is a variable for Welfare state Generosity and my independent variable is Welfare State Expenditure). What I am wondering is how is it possible to test the endogeneity and if the model is affected by it which solutions might be the most efficient (I have seen on this forum the possibility of using the commands -xtivreg- and -xtivreg2- but, knowing that I have to insert instrumental variables, I still find it difficult to identify the correct code)

[Carlo Lazzaro]

Mar:
the usual tricky issue rests on the choice of the instruments (valid and relevant).

[Mar Teeno]

Dear Carlo,
Many thanks for your answer. I think can find the appropriate instruments. What scares me is that the transposition of my ECM from xtreg (where I am sure it is defined correctly) to xtvreg is not correct:

 xtivreg socexp_ouh_f socexp_ouh_lag c.openc_f c.tot_gen_f c.openc_lag c.tot_gen_lag unemp_f unemp_lag de_ind_f de_ind_lag ///
          age_dep_f age_dep_lag infla_f infla_lag c.year (int_s_o_f int_s_o_lag = var 1 var 2), fe vce(cluster isocode)

alongside that there is the definition of the margins that is not possible anymore sice I have to recode the interaction term in order to fit it in the xtivireg command.
Thanks Again,
Mar