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-boun...@stat.math.ethz.ch [mailto:r-sig-finance-boun...@stat.math.ethz.ch] Im Auftrag von R_help Help Gesendet: Donnerstag, 8. Oktober 2009 02:55 An: r-sig-fina...@stat.math.ethz.ch; r-help@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 _______________________________________________ r-sig-fina...@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. -- If you want to post, subscribe first. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
