Hallo. Is there any package / code snippet to test the distribution assumption, heteroskedasticity, omitted variables, and linearity with the models estimated by maximum likelyhood? I especially need it for three type of models:
* binary choice (probit and probit with non-normal distribution) -- estimated by glm * tobit and tobit with non-normal distribution -- estimated by AER or any other suitable package * heckit and heckit with non-normal distribution To be more precise, when I estimate e.g. probit model, I should test that the random part in the data/model is normally distributed. If it was not, the parameter estimates may be biased. In the help and books I found how to estimate the parameters but not how to test the distribution assumption (and other assumptions stated above). Is there any package or code snippet, or do I need to derive the test for every distribution and model, and code it myself? Best wishes, Michal ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
