[R-SIG-Finance] Kalman Filter + DLM Package in R

nserdar snes1982 at hotmail.com
Wed Oct 31 09:36:01 CET 2012

Thanks for your response 


R(it)= Alpha(it)+ Beta(it)R(mt)+ V(it) 

KF Mean Reverting 

Alpha(it)= Alphabar(i)+ phi* (Alpha(it-1)-Alphabar(i))+W(i1t) 
Beta(it)= Betabar(i)+ phi* (Beta(it-1)-Betahabar(i))+W(i2t) 

Parameters are  estimated in  MLE function, these are 

Phi_1, Phi_2 ,  Alpha_bar, Beta_bar, Variance(Wi1t), Variance(Wi2t) and 
Variance (Vit)

Some restrictions I need in this process: 

0 <=  Phi_1  and Phi_2  < =1

Variances >0 

Alpha_bar and Beta_bar  are close to least square results. 

But some resources suggest Alpha is constant, otherwise multicollinearity
problem can be raised.


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