[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
Model:
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.
Regards,
Serdar
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