On Tue, Nov 03, 2009 at 11:54:52PM -0500, R_help Help wrote: > Hi, > > Assuming I have a time series on which I will perform rolling-window > MLE. In other words, if I stand at time t, I'm using points t-L+1 to t > for my MLE estimate of parameters at time t (here L is my rolling > window width). Next, at t+1, I'll do the same. > > My question is that is there anyway to avoid performing MLE each time > like does the above. My impression is that rolling from point t to > t+1, the likelihood function is equivalent to cutting out point t-L+1 > and add back likelihood at point t+1. Is there any smart way to > sequentially update the MLE instead of brute force calculation every > time? Any suggestion or reference would be appreciated. Thank you.
One thing you can certainly do is: Take the optimal parameter vector obtained using observations n to n+T and use it as the starting value for estimation from observations (n+1) to (n+T+1). The two $\hat theta$ values should be similar to each other, hence just one or two iterations should be required in making each step. -- Ajay Shah http://www.mayin.org/ajayshah ajays...@mayin.org http://ajayshahblog.blogspot.com <*(:-? - wizard who doesn't know the answer. ______________________________________________ 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.