took quite a bit to narrow down this issue.

MLWIN fails to estimate multilevel multivariate models with highly correlated dependents. I created an absurd toy example. This works:

**. use "http://www.bristol.ac.uk/cmm/media/runm ... ecomp1.dta", clear**

. runmlwin ///

(written cons, eq(1)) ///

(csework cons, eq(2)), ///

level1(student: (cons, eq(1)) (cons, eq(2)))

. runmlwin ///

(written cons, eq(1)) ///

(csework cons, eq(2)), ///

level1(student: (cons, eq(1)) (cons, eq(2)))

But this doesn't: (result is numerical error calculating likelihood)

**. gen int iwritten = written**

. corr written iwritten

. runmlwin ///

(written cons, eq(1)) ///

(iwritten cons, eq(2)), ///

level1(student: (cons, eq(1)) (cons, eq(2)))

. corr written iwritten

. runmlwin ///

(written cons, eq(1)) ///

(iwritten cons, eq(2)), ///

level1(student: (cons, eq(1)) (cons, eq(2)))

this fails too:

**. runmlwin ///**

(written cons, eq(1)) ///

(iwritten cons, eq(2)), ///

level1(student: (cons, eq(1 2)) )

(written cons, eq(1)) ///

(iwritten cons, eq(2)), ///

level1(student: (cons, eq(1 2)) )

I'm guessing it's an over-paramterized model issue, but regular mulivariate regression works:

**. mvreg written iwritten = cons, nocon**

so what is the multilevel equivalent? Is there anytype of adjustment ?

regards, ash