[R-SIG-Finance] Evaluating/comparing dynamic linear model

[Erb Philipp (erbp)]

What kind of filter are you using? Since your models are expressed in state space form I suggest that you fit your models by maximizing the log likelihood function of the Kalman filter output (see e.g. FKF-package). Using the obtained log likelihood values you might perform a likelihood ratio test to test the hypothesis whether model 1 explains yt "better" than model 2.

HTH, Phil


-----Ursprüngliche Nachricht-----
Von: r-sig-finance-bounces at stat.math.ethz.ch [mailto:r-sig-finance-bounces at stat.math.ethz.ch] Im Auftrag von R_help Help
Gesendet: Donnerstag, 8. Oktober 2009 02:55
An: r-sig-finance at stat.math.ethz.ch; r-help at r-project.org
Betreff: [R-SIG-Finance] Evaluating/comparing dynamic linear model

Hi,

I have two DLM model specifications (x[t] and y[t] are univariate):

MODEL1:
y[t] = b[t]x[t]+e[t], e[t] ~ N(0,v1^2)
b[t] = b[t-1]+eta[t], eta[t] ~ N(0,w1^2)

MODEL2:
y[t] = a[t]+e[t], e[t] ~ N(0,v2^2)
a[t] = a[t-1]+eta[t], eta[t] ~ N(0,w2^2)

I run the filter through data recursively to obtain state variables
for each model. However, how do I know if b[t]x[t] in MODEL1 is
different from MODEL2? In other words, how do I know if x[t] makes a
difference in explaining dynamic of y[t]?

Another question is that how do I compare MODEL1 and MODEL2? From
model specification point of view, how can one say that MODEL1 is
better than MODEL2? Any suggestion/reference would be greatly
appreciated. Thank you.

ac

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