Help with kalman-filterd betas using the dlm package

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

      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

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