Hi!
I have a panel dataset of N productive units over T periods of time. I estimate a model by maximum likelihood, and I estimate clustered standar errors, given the non independent nature of the sample, wich is the normal case in panel datasets.
Next, I want to test for the best specification of several nested models. Initially, I was using the likelihood ratio (LR) test in Stata using the "force" option. I came to realize that was wrong, because one of the assumptions of the test is that the observations are independent, which is not my case.
My question is, is there other test to test the performance of several nested models? is there any modification to the LR test in the clusters errors case?
Many thanks in advance to anyone who can shed light in this regard.
Have a look at https://www.stata.com/support/faqs/statistics/likelihood-ratio-test/
https://www.statalist.org/forums/forum/general-stata-discussion/general/1005-likelihood-ratio-test-for-the-presence-of-random-effects-and-robust-errors
https://arxiv.org/pdf/0911.3769.pdf
If you were using a method of specification that takes account of the clustering, such as multilevel/mixed/ random effects then the deviance can be compared in the usual way, the difference in the deviance is distributed as chi-square with df given as the difference in the number of parameters of the two nested models, see significance testing chapter in Book Developing multilevel models for analysing contextuality, he...
can refer to
http://cameron.econ.ucdavis.edu/research/Cameron_Miller_JHR_2015_February.pdf
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