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RE: st: Filtering methods with short time series


From   kokootchke <kokootchke@hotmail.com>
To   statalist <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Filtering methods with short time series
Date   Tue, 1 Feb 2011 21:49:19 -0500

Thanks, Jorge, Kit, and Nick for your answers. 
Regarding -cfitzrw-, I have also obtained the output from the CF filter in R and, although there are some slight differences, they are not substantial (at least in my case). The advantage is that R allows for non-RW, and I have run several tests and this RW assumption also does not seem to affect my results. 
Sorry for the confusion about the I(2) comment. I just meant to say that my series are not integrated and hence the robustness of the test to high degrees of integration, like that of an HP filter, are not something of concern to me... 
I have also used the -couliari- filter. This produces results very similar to -cfitzrw-, perhaps a bit less wavy, but not too different from each other.
My biggest concern is the short length of the series. I didn't know if there was a rule of thumb regarding the number of periods one should use to obtain "credible" trends/cycles. I also didn't know if there are any caveats to using short time series and the literature doesn't seem to be concerned on this regard.
Thanks once again.Adrian

----------------------------------------
> From: perez.jorge@ur.edu.co
> Date: Mon, 31 Jan 2011 15:42:45 -0500
> Subject: Re: st: Filtering methods with short time series
> To: statalist@hsphsun2.harvard.edu
>
> Try to avoid -cfitzrw-, I have compared the output from
> implementations of the Christiano Fritzgerald filter in other software
> to the Stata output and they are different. I already contacted the
> authors about this, but I haven't received any response.
>
> I don't understand why you argue that you need I(2) data to use the HP
> filter. You can use it on series with a lower order of integration,
> with the caveat that this might induce spurious autocorrelation on the
> filtered series.
>
> You might want to try the user written commands -bking- or -couliari-,
> that implement different approximations of the band pass filter. The
> latter estimates end points directly, thus avoiding the "future
> information" problem you write about. -bking- will only produce the
> filtered series, but you can obtain the cycle substracting the
> filtered series from the original series. -couliari- will produce both
> a trend and a cycle.
>
> To improve the smoothing to better suit your needs, you might want to
> try to vary the plo (s) and phi (e) options of bking(couliari), to
> extract cycles of the frequency you desire.
> _______________________
> Jorge Eduardo Pérez Pérez
>
>
>
>
> On Mon, Jan 31, 2011 at 3:12 PM, kokootchke  wrote:
> > Dear all:
> > I am inspecting time series data of relatively short length (monthly, 2003-2009). The data are the amount of time spent by individuals on various activities on a daily basis (cooking, sleeping, etc.), they are aggregated data, and they suffer from many pitfalls, e.g., the series are short, they look very different depending on each activity--and definitely not I(2) to be able to use an HP filter.
> > What I would like to do is to separate out any long-term trends from the short-term fluctuations, as usual. I have used the typical HP filter, calculated the optimal smoothing parameter (always something between 50,000 and 70,000 for my monthly series), and then use the -hprescott- command to generate the corresponding cycles and trends. This gives me some reasonable filtered data, with the expected phase shifts every now and then. Also, sometimes the cycles look way too much like the original series. Due to the nature of the data, I am certain that this is not the ideal filter in my case -- in fact, ideally, I would like to use an asymmetric filter that doesn't use future information to extract the trend.
> > I have also used a Christiano-Fitzgerald (-cfitzrw-) setting the low/high bars for the bandpass at 24/72 months, respectively. The filtered data produced with this filter are very strange, though: many of the series look wavy, like a sine/cosine function, and the trends often look very different from the apparent trends in the original series. Also, it's the first time I use this -cfitzrw- command and I'd like to know if it can also produce both a cycle and a trend series, the way -hprescott- does.
> > Could you provide any feedback as to how I can make these (or other) filters work better given my data?
> > Thank you.Adrian
> >
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