Hi Alex On 24 March 2011 04:47, Alex Olssen <alex.ols...@gmail.com> wrote: > I am looking for some recommended reading. > > I want to read up on the estimation systems of linear equations using > maximum likelihood?
For a comprehensive review of estimating systems of linear equations, see: http://www.jstatsoft.org/v23/i04/ or Greene, Econometric Analysis, 6th ed., 2008, chapter 10. For ML estimation in R, see: http://www.springerlink.com/content/973512476428614p/ For ML estimations of systems of linear equations, see: Greene, Econometric Analysis, 6th ed., 2008, section 13.6.2. Please note that the iterated SUR estimator should converge to the ML estimator. > I have a strongly applied bias, I want to be able to do such estimation > myself. > Reading with examples would be great. > Something which also works through a concentrated maximum likelihood > estimation would be even better! > > I want to use a likelihood ratio test to compare some nested models of > linear simultaneous equations. > I have estimated the systems using systemfit() and > nlsystemfit(). systemfit() can be used with lrtest() which > automatically obtains the log-likelihood values. > Unfortunately one of my models requires nonlinear coefficient > restrictions and lrtest() does not seem to be usable with > nlsystemfit(). Yes. Please note: in contrast to systemfit(), the function nlsystemfit() is still under development; it has convergence problems rather often and only a few methods have been implemented yet. > I decided it would be a good idea to program the likelihood estimation myself. > This would solve the problem and would also be a great learning experience. You are invited to implement the ML estimation in the "systemfit" package. You could join the developer team at R-Forge. Best wishes from Copenhagen, Arne -- Arne Henningsen http://www.arne-henningsen.name ______________________________________________ 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.
