> On Tue, 26 Feb 2013 16:59:25 -0800, Mike Hollis <firstname.lastname@example.org> > wrote: > >> I'm trying to formulate a simulation program in Stata. In particular, >> I want to simulation the behavior of a regression model with two >> independent variables under specific conditions. Stata has a function >> for developing oversavtions from a multivariate normal distribution > > - took a second, but I figure "oversavtions" is "observations" -
Yes. Sorry about the typo. > >> give a correlation/covariance matrix and vectors of means and standard >> deviations. But I'd rather formulate the model explicitly--something I >> haven't done since grad school, many years ago. > > You lose me here. What do *you* mean by "formulate ... explicitly"? > I figure that the first step is "developing observations" which you > then might do something else to (your "specific conditions").
i can use observtions generated from a multivariate nornal distirbution, pick a large n and run a regression to find values for the regression coefficients or I can explicitly formulate and solve for the betas using covariance algebra. It's the later that I'm interested in.
> >> >> Assume I have a standardize regression model with a known correlation >> matrix. I can set one beta and I'd like to derive the expression for > > What do you mean by "set one beta"? Fixing your correlation matrix > sort of sets both ....
Yes, if I generate observations from a multivariate normal dist. as described above. But what I'd like to do is set one of the betas and solve for the other > >> the seond in terms of the first. I also have a particular mean >> structure I want to impose. > > hmm... for *interesting* conditions that come to my mind, I > think of the means as being irrelevant.
Sorry. I don't follow this. > > >> Can anyone point me to books or articles >> outlining how to do this? >> > > > Maybe someone else isn't confused here, but for me, I > can't tell what you want to do.
I hope I've clarified things enough for someone to provide some references. Thanks in advance.