st: heteroskedasticity in ordered probit model

[John Stymans <johnstymans@hotmail.be>]

Dear Statalist Users,
I have a problem with heteroskedasticity in an oprobit model.
I am using Stata 12.
I am new to econometrics and Stata, however I would like to estimate an equation with following set-up (number of observations are around 200) ;
Dependent variable (y) is ordered (0-no, 1- sometimes, 2- a lot) over 2 countries, so I use a country dummy as independent variable. Other independent variables are: size, budget, service, R&D. 
Given the dependent I used an ordered probit model.
Setup in Stata is the following: oprobit y size budget service R&D country dummy
Yet literature (e.g. Cameron and Trivedi (2009) Microeconometrics Using  Stata, chapter 14 page 455) says the above model has a weakness: heteroskedasticity. So I decided to test this and I compared the model with one including a heteroskedasticity term. This term included budget. A Likelihood ratio test confirmed the presence of heteroskedasticy as did a Wald test.
Setup in Stata: oglm y size budget service R&D country dummy, link(probit) het(budget)
When comparing results, I noticed that 2 terms which are not significant in the first equation without heteroskedasticity term are actually significant in the equation with heteroskedasticity term. Is this possible: can heteroskedasticity affect the significance of variables, or is something else going on?
In a second step I would like to run a Mont Carlo simulation (I will have to figure this out, any advice on how to run such a thing is greatly appreciated) with a similar setup and check the above. Is this possible, can I use a Monte Carlo simulation to check if heteroskedasticity influences the significance of variables?
There is a lot of literature on the effect of heteroskedasticity on size of determinants (which often uses Monte Carlo simulations), yet I did not find any using Monte Carlo to check the significance (can however be my fault). 
Many thanks in advance and best regards,
JOhn 		 	   		  
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