For equally spaced observations this is quite simple and available in
various packages
but I like Gabor Grothendieck's version (which didn't come up
immediately in my Rseek search:
hpfilter <- function(y,lambda=1600)
eye <- diag(length(y))
solve(eye+lambda*crossprod(diff(eye,d=2)),y)}
url: www.econ.uiuc.edu/~roger Roger Koenker
email rkoen...@uiuc.edu Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678 Champaign, IL 61820
On Apr 4, 2009, at 2:19 PM, Rob Denniker wrote:
Can someone please show me how to smooth time series data that I
have in the form of a zoo object?
I have a monthly economies series and all I really need is to see a
less jagged line when I plot it.
If I do something like
s <- smooth.spline(d.zoo$Y, spar = 0.2)
plot(predict(s,index(d.zoo)), xlab = "Year")
# not defined for Date objects
and if I do something like
plot(predict(s,as.numeric(index(d.zoo))), xlab = "Year")
# one straight line, no matter the value of spar
What am I doing wrong? (The unsmoothed series plots just fine - a
noisy upward trend)
Thanks in advance.
P.S. I would really just like to keep varying the penalty parameter
'lambda' of a Hodrick-Prescott filter until I get a 'smooth enough'
series, but an archive search suggests that if I ask for this, some
smarty pants will reply saying that the HP filter is just the
smooth.spline function applied with a particular choice of lambda
and equally spaced observations.
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