1. Might you look again at section "2. Maximum likelihood
estimation" of the "dlm" vignette? It describes how to estimate
parameters.
2. Have you started with the code on those 2 pages, confirming
that you can make that work and understand what it does? If yes, then
try to build code for your problem as a series of small modifications to
that example. With luck, this will bring enlightenment. If not, try to
express your question in terms of commented, minimal, self-contained
code that others can copy into R to replicate what you see then modify
to get it to work, as suggested in the posting guide
"www.R-project.org/posting-guide.html". If someone reading this list
can do this in a few seconds, it will increases the chances that you
will get a useful reply.
Hope this helps.
Spencer Graves
tom81 wrote:
I have studied both the vinguette and other material I've been able to get my
hands on and Im starting to get a better understanding. And I'm defenitly
going to buy Petris, Petrone, and Campagnoli (2009) Dynamic Linear Models
with R. But that's not publish yet so I 'm not getting much help there.
This is the set-up i am using
y[t] = a[t] + b*x[t] + V[t],
a[t] = a[t-1] + W[t,a]
b[t] = b[t-1] + W[t,b]
V[t] ~ N(0,V)
W[t] ~ N(0,W)
W = blockdiag(W[a],W[b])
V could be estimated from the data with a non-diagonal variance matrix of
the returns,
W would be the same estimated in the same way but where the effect of past
betas in the transition taken into account. But how do I estimate that
matrix, is that done with a MLE,SUR or some other statistical teqnique.
Im also assuming in this example that a[t] are time invariant, which gives
W[a] = 0
Appriciate any guidence.
Regards Tom
"
spencerg wrote:
Have you worked through "vignette('dlm')"? Vignettes are nice
because they provide an Adobe Acrobat Portable Document Format (pdf)
file with a companion R script file, which you can get as follows:
(dlm. <- vignette('dlm'))
Stangle(dlm.$file)
The first of these two lines opens the "pdf" file. The second
creates a file "dlm.R" in the working directory (getwd()) containing the
R commands discussed in the "pdf" file.
If I remember correctly, your question is answered in this vignette.
You may also be interested in a book that is soon to appear about
this package: Petris, Petrone, and Campagnoli (2009) Dynamic Linear
Models with R (Springer;
http://www.amazon.com/Dynamic-Linear-Models-R-Use/dp/0387772375/ref=sr_1_4?ie=UTF8&s=books&qid=1242162708&sr=1-4),
scheduled to ship in late June. If you have long-term interest in this
subject, as I suspect you may, you might find this book interesting and
useful.
Hope this helps.
Spencer Graves
"
tom81 wrote:
Hi all R gurus out there,
Im a kind of newbie to kalman-filters after some research I have found
that
the dlm package is the easiest to start with. So be patient if some of my
questions are too basic.
I would like to set up a beta estimation between an asset and a market
index
using a kalman-filter. Much littarture says it gives superior estimates
compared to OLS estimates. So I would like to learn and to use the
filter.
I would like to run two types of kalman-filters, one with using a
random-walk model (RW) and one with a stationary model, in other worlds
the
transition equition either follow a RW or AR(1) model.
This is how I think it would be set up;
I will have my time-series Y,X, where Y is the response variable
this setup should give me a RW process if I have understood the example
correctly
mydlmModel = dlmModReg(X) + dlmModPoly(order=1)
and then run on the dlm model
dlmFilter(Y,mydlmModel )
but setting up a AR(1) process is unclear, should I use dlmModPoly or the
dlmModARMA to set up the model.
And at last but not the least, how do I set up a proper build function to
use with dlmMLE to optimize the starting values.
Regards Tom
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