Achim Zeileis-4 wrote > > > The reason for the various approaches is that efp() was always confined to > the linear model and gefp() then extended it to arbitrary estimating > function-based models. And for the linear model this provides the option > of treating the variance of a nuisance parameter or a full model > parameter. > >
I'm not sure, if my understanding of treating the variance as a nuisance parameter (in the Nyblom-Hansen test) is right. The main interest of my analysis relies on the stability of regression coefficients (and not of the variance). Is it therefore prefereable to treat the variance as a nuisance parameter rather than a full model parameter? -- View this message in context: http://r.789695.n4.nabble.com/strucchange-Nyblom-Hansen-Test-tp3887208p4394315.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.