|Title||Chi-squared test for models estimated with robust standard errors|
|Author||William Sribney, StataCorp|
|Date||August 1997; minor revisions May 2005; minor revisions August 2007|
When you specify vce(robust), specify vce(cluster clustvar), or use pweights for a maximum likelihood estimation command that allows these options, the model chi-squared test is a Wald test rather than a likelihood-ratio test.
When you have clusters or pweights, the likelihood used for estimation is not a true likelihood; i.e., it is not the distribution of the sample. For clustering, observations are no longer independent. For pweights, the likelihood does not reflect the "randomness" of the sampling weights. Thus, here, one should not use the conventional likelihood-ratio test.
When you only have a few clusters (say, <100), an adjusted Wald test is better than the standard Wald test. The svy commands use the adjusted Wald test by default, as does the test command when used after svy estimation. For more information, see [R] test also Korn and Graubard (1990).