Hi, Without knowing much about the problem, it is difficult to provide good advice. Having said that, it seems like you are trying to solve a system of nonlinear equations by matching theoretical moments to their empirical counterparts. You can do this by using a nonlinear equations solver such as dfsane() in the the package "BB" or nleqslv() in "nleqslv".
It is not clear to me how you end up with a scalar objective function to minimize (do you consider the L2-norm of the residuals?). Ravi. ____________________________________________________________________ Ravi Varadhan, Ph.D. Assistant Professor, Division of Geriatric Medicine and Gerontology School of Medicine Johns Hopkins University Ph. (410) 502-2619 email: rvarad...@jhmi.edu ----- Original Message ----- From: ivo...@gmail.com Date: Wednesday, March 25, 2009 6:16 pm Subject: [R] intelligent optimizer (with domain restrictions?) To: r-help <r-h...@stat.math.ethz.ch> > dear R experts---sorry, second question of the day. I want to match > some > moments. I am writing my own code---I have exactly as many moment > conditions as parameters, and I am leary of having to learn the magic > of > GMM weighting matrices (if I was to introduce more). the process > sounds > easy conceptually. (Seen it in seminars many times, so how hard could > it > possibly be?...me thinks) first time I am trying this. some of my > moments > are standard deviations. Easy, me thinks. Just maximize the > exp(my.sigma.parameter) instead of the my.sigma.parameter. This way, > nlm() > can throw negative values into my objective function, and I will be > good. > this is about the time to start laughing, of course. > > so, nlm() computes a gradient that is huge at my initial starting > value. it > then decides that it wants to take a step into exp(20.59), at which > point > everything in my function goes heywire and it wants to return NA. now > nlm() > barfs...and I am seriously consider grid-searching. This does not > strike me > as particular intelligent. > > are there any intelligent optimizers that understand domains and/or > > will "backstep" gracefully when they encounter an NA? are there > better ways > to deal with matching second moments? > > advice appreciated. > > regards, > > /iaw > > PS: you probably don't want to know this, but I have a dynamic panel > data > set; and my goal is to test whether a constant auto-coefficient > across > units can describe the data. that is, I want to find out whether > x(i,t)= a > + b(i) + c*x(i,t-1) is better replaced by x(i,t)=a + b(i) + > c(i)*x(i,t-1). > right now, I am running N OLS TS regression of x on lagged x, and am > > picking off the mean(c), sd(c), and mean(sigma_i) and sd(sigma_i). if > there > is a procedure in R that already does a test for heterogeneous > autocorrelation coefficients in a more intelligent fashion, please > please > point me to it. however, even if this exists, I think I need to > figure out > how to find a more graceful optimizer anyway. > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > > PLEASE do read the posting guide > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ 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.
