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Re: st: [Non Stata] Testing nested models with clustering

From   Stas Kolenikov <>
Subject   Re: st: [Non Stata] Testing nested models with clustering
Date   Tue, 16 Jun 2009 13:48:16 -0500

You can do a one-sided Wald test with the clustered standard errors
just fine. The LRT is indeed a disaster, and if you have clustering
you would probably have a pseudo-likelihood rather than true
likelihood, anyway (although I am not really familiar with frailty
models sufficiently well). This means that the distribution of the LRT
even without a boundary is not a chi-square but a sum of weighted
chi-squares, Satterthwaite-style. If you truncate this to the
boundary, all the hell breaks lose. The Wald test however is quite
simple, clustered standard errors, jackknife standard errors, or
whatever standard errors you trust in your situation. If the left
confidence limit covers zero, you don't reject, and can claim there is
no heterogeneity. I suspect there is one, anyways... otherwise you
won't be doing the more complicated analysis, right? :))

On 6/16/09, Antoine Terracol <> wrote:
> Dear _all,
>  I have estimated two models using -ml-. The two models are nested, and
> model B is equivalent to model A if some parameter delta is equal to zero.
>  I have clustered standard errors, and thus cannot use a LR test to test B
> against A. Moreover, delta is constrained in (0,1) through a logistic
> transformation.
>  In -heckman- with -cluster()-, the corresponding test is a Wald test of
> atanh(rho)=0; but in -streg, frailty()- with -cluster()-, no test is
> performed for the null that there is no heterogeneity (without clustering, a
> LR test for theta=0 is performed).
>  Should I infer  that there is no proper (and simple) way to test for nested
> models with clustering when the additional parameter is constrained, and the
> null is on the boundary?
>  Best,
>  Antoine
>  --
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Stas Kolenikov, also found at
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