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st: predictnl - calculate mfx / s.e. - oprobit with interaction


From   "Zohal Hessami" <zohal_hessami@gmx.de>
To   statalist@hsphsun2.harvard.edu
Subject   st: predictnl - calculate mfx / s.e. - oprobit with interaction
Date   Tue, 24 Feb 2009 13:50:17 +0100

> Hello guys,
> 
> I am currently estimating an ordered probit model, where I have  
> included an interaction term. The regression equation looks as follows:
> 
> oprobit lifesat male age age_sq income married divorced separated  
> widowed ageeduc_1619 ageeduc_lt19 unempl school retired home self_empl  
> govtexp expdecentral expdecexp, r
> 
> I have included the interaction term (expdecexp =  
> expdecentral*govtexp) because I want to test whether the impact of  
> government size on people's life satisfaction is more likely to be  
> positive if there is a large extent of expenditure decentralization in  
> this specific country. As you can see my dataset is generally a  
> mixture of micro- and macro-level variables.
> 
> I am now trying to estimate the marginal effects for govtexp,  
> expdecentral and the interaction term expdecexp. For this purpose I  
> found the link  
> http://www.stata.com/support/faqs/stat/mfx_interact.html very useful.  
> In the middle section of this website, it is explained how this  
> calculation is carried out in a binary probit setting with an  
> interaction term. Accordingly, I have asked STATA to do the following:
> 
> local xb _b[male]*`meanmale' + _b[age]*`meanage' +  
> _b[age_sq]*`meanagesq'  + _b[income]*`meanincome' +  
> _b[married]*`meanmarried' + _b[divorced]*`meandivorced' +  
> _b[separated]*`meanseparated' + _b[widowed]*`meanwidowed' +  
> _b[ageeduc_1619]*`meanageeduc_1619' +  
> _b[ageeduc_lt19]*`meanageeduc_lt19' + _b[unempl]*`meanunempl' +  
> _b[school]*`meanschool' + _b[retired]*`meanretired' +  
> _b[home]*`meanhome' + _b[self_empl]*`meanself_empl'  +  
> _b[govtexp]*`meangovtexp' + _b[expdecentral]*`meanexpdecentral' +  
> _b[expdecexp]*`meangovtexp'*`meanexpdecentral'
> 
> predictnl dydg = normalden(_b[/cut3] - `xb')*(_b[govtexp] +  
> _b[expdecexp]*`meanexpdecentral') in 1, se(seg)
> 
> predictnl dyde = normalden(_b[/cut3] - `xb')*(_b[expdecentral] +  
> _b[expdecexp]*`meangovtexp') in 1, se(see)
> 
> predictnl dydeg = normalden(_b[/cut3] - `xb')*(-(_b[/cut3] -  
> `xb'))*(_b[govtexp] +  
> _b[expdecexp]*`meanexpdecentral')*(_b[expdecentral] +  
> _b[expdecexp]*`meangovtexp') + normalden(_b[/cut3] -  
> `xb')*(_b[expdecexp]) in 1, se(seeg)
> 
> I know for sure that my derivations for the equations that I have used  
> for the marginal effects are correct. Nevertheless, the value for the  
> marginal effects is reported as ".", while the standard errors that I  
> get are "0". Furthermore, I get the error messages >>> Warning:  
> prediction doesn't vary with respect to e(b) <<<  and >>> Warning:  
> prediction constant over observations; perhaps you meant to run  
> nlcom.<<<<
> 
> I also programmed the whole thing with nlcom as described on this  
> website that I have mentioned above, but I still didn't get any  
> results. When I did the same calculations when interacting govtexp and  
> people's income the whole procedure worked perfectly and I got very  
> convincing results. Apparently, the fact that I am interacting two  
> macro-level variables is causing trouble. Can anyone help me solve  
> this problem or tell me how to get the marginal effects that I need???
> 
> Thx in advance,

> Zohal Hessami
> University of Konstanz
> Department of Economics

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