Hey,
Try the AIC function for your model object. ?AIC
Check package fitAR, there are criterion options for BIC.
Why use BIC over AIC? I am not sure if it is clear that one is
better. Also, if your model is small it shouldn't really make a difference.
RegaRds,
Ken Hutchison
On Wed, Oct 19, 2011 at 7:22 PM, J Toll <jct...@gmail.com> wrote:
> Hi,
>
> I'm slowly working through Tsay's "Analysis of Financial Time Series"
> 3rd ed. I'm trying to replicate Table 2.1 on p.47, which gives PACF,
> AIC, and BIC for the monthly simple returns of the CRSP value-weighted
> index.
>
> The data:
> http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/m-ibm3dx2608.txt
>
> > da <- read.table("
> http://faculty.chicagobooth.edu/ruey.tsay/teaching/fts3/m-ibm3dx2608.txt";,
> header = TRUE)
> > vw <- da[, 3]
>
> I can replicate the PACF calculations.
> > x <- pacf(vw)
> > x
> Partial autocorrelations of series vw, by lag
>
> 1 2 3 4 5 6 7 8 9 10
> 0.115 -0.030 -0.102 0.033 0.062 -0.050 0.031 0.052 0.063 0.005
> 11 12 13 14 15 16 17 18 19 20
> -0.005 0.011 -0.048 -0.084 0.012 -0.055 0.078 0.021 -0.048 -0.062
> 21 22 23 24 25 26 27 28 29
> -0.060 0.003 -0.025 0.024 -0.041 0.016 -0.023 0.023 0.029
>
> The ar() function returns the same order as indicated in the
> book(based on AIC), but the AIC values appear to be adjusted so that
> the minimum AIC value is 0.
>
> > m1 <- ar(vw, method = "mle")
> > m1$order
> [1] 9
> > m1$aic
> 0 1 2 3 4 5 6
> 22.329967 10.990260 12.066700 3.350972 4.365413 2.462650 1.960128
> 7 8 9 10 11 12
> 3.041666 2.243258 0.000000 1.966641 3.942486 5.811573
>
> According to the book the AIC values are:
> 0 1 2 3 4 5 6
> NA -5.838 -5.837 -5.846 -5.845 -5.847 -5.847
> 7 8 9 10 11 12
> -5.846 -5.847 -5.849 -5.847 -5.845 -5.843
>
> Is there a way to get "unadjusted" AIC values(i.e. values that match
> the text)? Additionally, is there a way to force ar() to use BIC and
> return those values?
>
> Thank you.
>
> James
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