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Thanks Johannes. Stéphane.
On 19/02/2014 14:30, Johannes Pfeifer wrote:
The book uses a univariate example in the beginning. But the later chapter (4 I think) uses a
selector matrix for the observed variables as you said.
Am 19.02.2014 14:26, schrieb Stéphane Adjemian: Dear Johannes,
I do not have the edition of the book by Durbin and Koopman you are referring to in the manual. Frédéric seems to think that the missing observations are treated (in the book) with the univariate filter, could you tell me if this is the correct ? The Dynare implementation of the filters/smoothers with missing observations does not use the univariate filter (which is very slow on matlab) but a standard Kalman filter with time varying selection matrix (we remove the rows corresponding to the missing observations when needed).
I agree with Michel, I do not plan to implement an interface for handling mixed frequencies. I have a SW model, for which I developed these filters, with mixed frequencies (quaterly and yearly date). I will try to post it somewhere as an example.
Best, Stéphane.
On 19/02/2014 11:51, Johannes Pfeifer wrote:
Hi,
I agree with Stéphane. The current Kalman filter implementation treats
missing observations as unobserved states. This is standard and the book reference is just one of the standard references that can be cited there.
As Stéphane says, mixed frequency filtering is something different and
not (yet) implemented as it requires explicitly setting up observation equations within the model-block to link the different frequencies.
That being said, you might already be able to do mixed frequency
filtering and smoothing in Dynare if you can nest it within the standard Kalman filtering framework. But there is no dedicated functionality and syntax.
Best,
Johannes
Johannes Pfeifer Department of Economics University of Mannheim L7, 3-5, Room 242 68131 Mannheim Germany +49 (0)621 181-3430
Am 19.02.2014 11:11, schrieb Stéphane Adjemian: Hi,
I never had a chance to the read the book mentioned by
Sébastien... And
I won't buy it. So I do not know if we follow the methodology
described
by Durbin and Koopman (kalman filters with missing observations were implemented in Dynare in 2010 or even before I do not remember, and there is an old literature about this problem). Adding NaNs in the datafile is not the whole story if you want to mix frequencies.
You also
need to adapt the model. For instance, if your model is quaterly
but you
also have yearly observed data, you need to add an equation to relate the yearly data to the quaterly variables (except if the yearly
variable
is a stock which is not the case in general). More generally you
need to
write the model in the highest frequency and add measurement equations in order link the variables with different frequencies.
Best, Stéphane.
On 19/02/2014 10:43, Sébastien Villemot wrote:
> Hi Tobias, > > Le mardi 18 février 2014 à 17:48 +0000, Tobias Cwik a écrit : > >> I hope this email finds you well. I don’t know if you remember
me, I
>> was working for Prof. Wieland in Frankfurt. I heard that the
Dynare
>> team is working on mixed frequency filtering and smoothing in
Dynare.
>> Do you know if there is anything implemented in Dynare already? > Yes, Dynare can handle missing observations in the dataset. Which
means
> that if you have a quarterly series and a annual series, you
can mix
> them, by putting missing observations for the annual series in Q2,
Q3 an
> d Q4. Dynare will be able to run the filter, using the methodology > described in Durbin and Koopman (2012): "Time Series Analysis
by State
> Space Methods", second edition, Ch. 4.10. > > I put Stéphane in CC, he is the one who programmed this. > > Best, >
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