st: Filtering methods with short time series

[kokootchke <kokootchke@hotmail.com>]

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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