Ok, it seems that R's AIC and BIC functions warn about different constants,
so that's probably enough. The constants are not irrelevant though, if you
compute the log-likelihood of one model using StructTS, and then fit
alternative model using other functions such as arima(), which do take
account the constant term, and use those loglikelihoods for computing for
example BIC, you get wrong results when checking which model gives lower
BIC value. Hadn't though about it before, have to be more careful in future
when checking results from different packages etc.
Jouni
On Tue, May 1, 2012 at 4:51 PM, Ravi Varadhan <rvarad...@jhmi.edu> wrote:
> This is not a problem at all. The log likelihood function is a function
> of the model parameters and the data, but it is defined up to an additive
> arbitrary constant, i.e. L(\theta) and L(\theta) + k are completely
> equivalent, for any k. This does not affect model comparisons or hypothesis
> tests.
>
> Ravi
> ________________________________________
> From: r-devel-boun...@r-project.org [r-devel-boun...@r-project.org] on
> behalf of Jouni Helske [jounihel...@gmail.com]
> Sent: Monday, April 30, 2012 7:37 AM
> To: r-devel@r-project.org
> Subject: [Rd] The constant part of the log-likelihood in StructTS
>
> Dear all,
>
> I'd like to discuss about a possible bug in function StructTS of stats
> package. It seems that the function returns wrong value of the
> log-likelihood, as the added constant to the relevant part of the
> log-likelihood is misspecified. Here is an simple example:
>
> > data(Nile)
> > fit <- StructTS(Nile, type = "level")
> > fit$loglik
> [1] -367.5194
>
> When computing the log-likelihood with other packages such as KFAS and FKF,
> the loglikelihood value is around -645.
>
> For the local level model, the likelihood is defined by -0.5*n*log(2*pi) -
> 0.5*sum(log(F_t) + v_t^2/sqrt(F_t)) (see for example Durbin and Koopman
> (2001, page 30). But in StructTS, the likelihood is computed like this:
>
> loglik <- -length(y) * res$value + length(y) * log(2 * pi),
>
> where the first part coincides with the last part of the definition, but
> the constant part has wrong sign and it is not multiplied by 0.5. Also in
> case of missing observations, I think there should be sum(!is.na(y))
> instead of length(y) in the constant term, as the likelihood is only
> computed for those y which are observed.
>
> This does not affect in estimation of model parameters, but it could have
> effects in model comparison or some other cases.
>
> Is there some reason for this kind of constant, or is it just a bug?
>
> Best regards,
>
> Jouni Helske
> PhD student in Statistics
> University of Jyväskylä
> Finland
>
> [[alternative HTML version deleted]]
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